Recommender System with Machine Learning and Artificial Intelligence E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 588

Veröffentlichungsjahr: 2020

Beschreibung

This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior.  It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior.  Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising.

This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.

Leseprobe

Table of Contents

Cover

Preface

Acknowledgment

Part I: INTRODUCTION TO RECOMMENDER SYSTEMS

1 An Introduction to Basic Concepts on Recommender Systems

1.1 Introduction

1.2 Functions of Recommendation Systems

1.3 Data and Knowledge Sources

1.4 Types of Recommendation Systems

1.5 Item-Based Recommendation vs. User-Based Recommendation System

1.6 Evaluation Metrics for Recommendation Engines

1.7 Problems with Recommendation Systems and Possible Solutions

1.8 Applications of Recommender Systems

References

2 A Brief Model Overview of Personalized Recommendation to Citizens in the Health-Care Industry

2.1 Introduction

2.2 Methods Used in Recommender System

2.3 Related Work

2.4 Types of Explanation

2.5 Explanation Methodology

2.6 Proposed Theoretical Framework for Explanation-Based Recommender System in Health-Care Domain

2.7 Flowchart

2.8 Conclusion

References

3 2Es of TIS: A Review of Information Exchange and Extraction in Tourism Information Systems

3.1 Introduction

3.2 Information Exchange

3.3 Information Extraction

3.4 Sentiment Annotation

3.5 Comparison of Different Annotations Schemes

3.6 Temporal and Event Extraction

3.7 TimeML

3.8 Conclusions

References

Part 2: MACHINE LEARNING-BASED RECOMMENDER SYSTEMS

4 Concepts of Recommendation System from the Perspective of Machine Learning

4.1 Introduction

4.2 Entities of Recommendation System

4.3 Techniques of Recommendation

4.4 Performance Evaluation

4.5 Challenges

4.6 Applications

4.7 Conclusion

References

5 A Machine Learning Approach to Recommend Suitable Crops and Fertilizers for Agriculture

5.1 Introduction

5.2 Literature Review

5.3 Methodology

5.4 Results and Analysis

5.5 Conclusion

References

6 Accuracy-Assured Privacy-Preserving Recommender System Using Hybrid-Based Deep Learning Method

6.1 Introduction

6.2 Overview of Recommender System

6.3 Collaborative Filtering-Based Recommender System

6.4 Machine Learning Methods Used in Recommender System

6.5 Proposed RBM Model-Based Movie Recommender System

6.6 Proposed CRBM Model-Based Movie Recommender System

6.7 Conclusion and Future Work

References

7 Machine Learning-Based Recommender System for Breast Cancer Prognosis

7.1 Introduction

7.2 Related Works

7.3 Methodology

7.4 Results and Discussion

7.5 Conclusion

Acknowledgment

References

8 A Recommended System for Crop Disease Detection and Yield Prediction Using Machine Learning Approach

8.1 Introduction

8.2 Machine Learning

8.3 Recommender System

8.4 Crop Management

8.5 Application—Crop Disease Detection and Yield Prediction

References

Part 3: CONTENT-BASED RECOMMENDER SYSTEMS

9 Content-Based Recommender Systems

9.1 Introduction

9.2 Literature Review

9.3 Recommendation Process

9.4 Techniques Used for Item Representation and Learning User Profile

9.5 Applicability of Recommender System in Healthcare and Agriculture

9.6 Pros and Cons of Content-Based Recommender System

9.7 Conclusion

References

10 Content (Item)-Based Recommendation System

10.1 Introduction

10.2 Phases of Content-Based Recommendation Generation

10.3 Content-Based Recommendation Using Cosine Similarity

10.4 Content-Based Recommendations Using Optimization Techniques

10.5 Content-Based Recommendation Using the Tree Induction Algorithm

10.6 Summary

References

11 Content-Based Health Recommender Systems

11.1 Introduction

11.2 Typical Health Recommender System Framework

11.3 Components of Content-Based Health Recommender System

11.4 Unstructured Data Processing

11.5 Unsupervised Feature Extraction & Weighting

11.6 Supervised Feature Selection & Weighting

11.7 Feedback Collection

11.8 Training & Health Recommendation Generation

11.9 Evaluation of Content-Based Health Recommender System

11.10 Design Criteria of CBHRS

11.11 Conclusions and Future Research Directions

References

12 Context-Based Social Media Recommendation System

12.1 Introduction

12.2 Literature Survey

12.3 Motivation and Objectives

12.4 Performance Measures

12.5 Precision

12.6 Recall

12.7 F- Measure

12.8 Evaluation Results

12.9 Conclusion and Future Work

References

13 Netflix Challenge—Improving Movie Recommendations

13.1 Introduction

13.2 Data Preprocessing

13.3 MovieLens Data

13.4 Data Exploration

13.5 Distributions

13.6 Data Analysis

13.7 Results

13.8 Conclusion

References

14 Product or Item-Based Recommender System

14.1 Introduction

14.2 Various Techniques to Design Food Recommendation System

14.3 Implementation of Food Recommender System Using Content-Based Approach

14.4 Results

14.5 Observations

14.6 Future Perspective of Recommender Systems

14.7 Conclusion

Acknowledgements

References

Part 4: BLOCKCHAIN & IOT-BASED RECOMMENDER SYSTEMS

15 A Trust-Based Recommender System Built on IoT Blockchain Network With Cognitive Framework

15.1 Introduction

15.2 Technologies and its Combinations

15.3 Crypto Currencies With IoT–Case Studies

15.4 Trust-Based Recommender System

15.5 Recommender System Platform

15.6 Conclusion and Future Directions

References

16 Development of a Recommender System HealthMudra Using Blockchain for Prevention of Diabetes

16.1 Introduction

16.2 Architecture of Blockchain

16.3 Role of HealthMudra in Diabetic

16.4 Blockchain Technology Solutions

16.5 Conclusions

References

Part 5: HEALTHCARE RECOMMENDER SYSTEMS

17 Case Study 1: Health Care Recommender Systems

17.1 Introduction

17.2 Review of Literature

17.3 Recommender System for Parkinson’s Disease (PD)

17.4 Future Perspectives

17.5 Conclusions

References

18 Temporal Change Analysis-Based Recommender System for Alzheimer Disease Classification

18.1 Introduction

18.2 Related Work

18.3 Mechanism of TCA-RS-AD

18.4 Experimental Dataset

18.5 Neural Network

18.6 Conclusion

References

19 Regularization of Graphs: Sentiment Classification

19.1 Introduction

19.2 Neural Structured Learning

19.3 Some Neural Network Models

19.4 Experimental Results

19.5 Conclusion

References

20 TSARS: A Tree-Similarity Algorithm-Based Agricultural Recommender System

20.1 Introduction

20.2 Literature Survey

20.3 Research Gap

20.4 Problem Definitions

20.5 Methodology

20.6 Results & Discussion

20.7 Conclusion & Future Work

References

21 Influenceable Targets Recommendation Analyzing Social Activities in Egocentric Online Social Networks

21.1 Introduction

21.2 Literature Review

21.3 Dataset Collection Process with Details

21.4 Primary Preprocessing of Data

21.5 Influence and Social Activities Analysis

21.6 Recommendation System

21.7 Top Most Influenceable Targets Evaluation

21.8 Conclusion

21.9 Future Scope

References

Index

End User License Agreement

List of Tables

Chapter 3

Table 3.1 Examples of available tourist information systems (TIS).

Table 3.2 Example of mapping from TIF to Schema.org.

Chapter 4

Table 4.1 User-preference information for memory based filtering.

Table 4.2 Confusion matrix.

Chapter 6

Table 6.1 Differentiation between K and RMSE using various methods.

Table 6.2 Comparison among K and MAE of different methods.

Table 6.3 Comparison among different parameters of different methods and no. ...

Table 6.4 Comparison among no. of epoch and MAE value of different methods.

Chapter 7

Table 7.1 Clinical attributes of BCCD.

Table 7.2 Comparison of RMSE and MAE for prediction algorithms.

Table 7.3 Comparison of BPE and WPE for prediction algorithms.

Table 7.4 5-Fold cross-validation measures on prediction algorithms.

Table 7.5 Rating scale formulation.

Chapter 8

Table 8.1 Difference between supervised learning and unsupervised learning.

Table 8.2 Summary of crops with their functionality and algorithm [8].

Table 8.3 Summary of crops with their functionality and algorithm.

Chapter 10

Table 10.1 Item/product vs features.

Table 10.2 Simple mean.

Table 10.3 Weighted mean.

Table 10.4 Star ratings.

Table 10.5 Normalized ratings.

Table 10.6 TF-IDF parameters.

Table 10.7 Book ratings by users.

Table 10.8 Parameter vector estimation.

Table 10.9 Chance of purchasing a car.

Table 10.10 Information content calculation for age.

Table 10.11 Information content calculation for income.

Chapter 11

Table 11.1 An example of term document matrix created from medical records.

Table 11.2 An example of stemming process created from medical records.

Table 11.3 An example of creating vector from words.

Chapter 12

Table 12.1 User profiling.

Chapter 13

Table 13.1 RMSE with Naive approach.

Table 13.2 RMSE with movie effects model.

Table 13.3 RMSE with Movie + User effects model.

Table 13.4 RMSE with Regularized Movie + User effects model.

Table 13.5 Results.

Chapter 15

Table 15.1 Study on crypto currencies employed in IoT.

Table 15.2 Comparison of recommendation systems.

Chapter 17

Table 17.1 Comparison of different classifiers.

Table 17.2 Differential diagnosis of Parkinson’s.

Table 17.3 FDA-approved medications for Parkinson’s disease.

Table 17.4 Management of non-motor symptoms of Parkinson’s disease.

Chapter 18

Table 18.1 Demographic characteristics.

Table 18.2 Clinical information of OASIS dataset.

Table 18.3 Sample time series OASIS dataset.

Table 18.4 Sample time series data using

groupby()

function.

Table 18.5 Model summary of the TCA-RS-AD.

Table 18.6 Hyperparameters for TCA-RS-AD.

Table 18.7 Description of performance metrics.

Table 18.8 Performance analysis of DNN and TCA-RS-AD model.

Table 18.9 Comparison analysis of error rate metrics.

Table 18.10 Performance summary of the classifiers.

Chapter 20

Table 20.1 Precision, Accuracy, PPV & FDR values of five users.

Chapter 21

Table 21.1 Representation of influenceable targets and Online Social Media (O...

List of Illustrations

Chapter 1

Figure 1.1 Different recommendation techniques.

Figure 1.2 Different techniques of collaborative filtering.

Chapter 2

Figure 2.1 Content-based recommendation.

Figure 2.2 Collaborative filtering.

Figure 2.3 Category of explanation.

Figure 2.4 Information acquisition in knowledge-based explanation.

Figure 2.5 Online explanation-based recommender system for health-care.

Chapter 3

Figure 3.1 Examples of semantic clashes [16].

Figure 3.2 Examples of structural clashes [16].

Chapter 4

Figure 4.1 Entities of recommendation system.

Figure 4.2 Recommendation mechanism.

Figure 4.3 Recommendation techniques types based on information filtering.

Figure 4.4 Content-based filtering mechanism (book recommendation).

Figure 4.5 Collaborative filtering mechanism (book recommendation).

Figure 4.6 Challenges and applications of recommendation system.

Chapter 5

Figure 5.1 Sample of Soil Health Card

Figure 5.2 Status of SHC Collected and Tested

Figure 5.3 Status of SHC Printed and Distributed

Figure 5.4 Comparative results.

Chapter 6

Figure 6.1 Block Diagram of a recommender system.

Figure 6.2 Phases of the recommender system.

Figure 6.3 Different filtering techniques used in recommender system.

Figure 6.4 Process flow of collaborative filtering.

Figure 6.5 Auto-encoder neural network.

Figure 6.6 RBM neural network.

Figure 6.7 Comparison among ‘k’ and RMSE value.

Figure 6.8 Comparison graph between MAE and no. of epochs.

Figure 6.9 Flowchart of proposed CRBM approach.

Figure 6.10 Comparison graph between MAE and no. of epochs.

Chapter 7

Figure 7.1 Hierarchy of recommender systems.

Figure 7.2 Correlation matrix heatmap for BCCD.

Figure 7.3 Phases of MLRS-BC.

Figure 7.4 Flowchart of MLRS-BC.

Figure 7.5 Comparison of RMSE and MAE for Prediction Algorithms.

Figure 7.6 Comparison of BPE and WPE for prediction algorithms.

Figure 7.7 5-Fold Cross-Validation Technique.

Figure 7.8 Rating of attributes using BaselineOnly.

Figure 7.9 Rating of attributes using KNNBasic.

Figure 7.10 Rating of attributes using SVD.

Figure 7.11 Overall comparison of ratings.

Chapter 8

Figure 8.1 Machine learning algorithms [8, 9].

Figure 8.2 Simple Artificial Neural Network [11].

Figure 8.3 Simple random forest concept [11].

Figure 8.4 General overview of recommender system in agriculture [3].

Figure 8.5 Architecture of crop disease detection and prediction system.

Figure 8.6 Commands to run the model.

Figure 8.7 Fruit identification and disease detection in strawberry crop.

Figure 8.8 Identify and detect disease in citrus canker.

Chapter 9

Figure 9.1 Content based recommender system.

Figure 9.2 Architecture of content based recommender system [87].

Figure 9.3 Architecture of profile cleaner [87].

Chapter 10

Figure 10.1 Induction tree algorithm.

Figure 10.2 Python function to calculate Info (D).

Figure 10.3 Induction tree for ‘buying a car example’.

Chapter 11

Figure 11.1 The flow chart of CBHRS system.

Chapter 12

Figure 12.1 Location-Based Recommendation System (LBRS) services.

Figure 12.2 Proposed architecture.

Figure 12.3 Extraction of Twitter streaming data.

Figure 12.4 Recommendation of POI to users.

Figure 12.5 Similarity computation among users.

Figure 12.6 Neighborhood selection.

Chapter 13

Figure 13.1 Matrix for a random sample of 100 movies and 100 users.

Figure 13.2 Distribution of movie ratings.

Figure 13.3 Distribution of users.

Figure 13.4 Movies Bias—Distribution of estimates b_i.

Figure 13.5 Users Bias—Distribution of estimates b_u.

Figure 13.6 Optimal value of penalty term lambda,

λ

.

Chapter 14

Figure 14.1 Flow chart of proposed method.

Figure 14.2 word2vec Model.

Figure 14.3 Hamming distance output.

Figure 14.4 Jaccard distance output.

Figure 14.5 Cosine similarity output.

Chapter 15

Figure 15.1 Examples of IoT things.

Figure 15.2 Blockchain transaction.

Figure 15.3 Combination of technologies.

Figure 15.4 Structure of IoT blockchain network on cognitive framework.

Figure 15.5 Layer of things management.

Figure 15.6 Comparison of recommendation systems.

Chapter 16

Figure 16.1 Architecture of blockchain.

Figure 16.2 Database versus Blockchain architecture.

Figure 16.3 Data structure of Blockchain (Hashing).

Figure 16.4 A schema showing the chain of blocks to form a blockchain [17]....

Chapter 18

Figure 18.1 Illustration of one-hot encoding for categorical data.

Figure 18.2 Flowchart of TCA-RS-AD.

Figure 18.3 Basic neural network model.

Figure 18.4 Performance of DNN and TCA-RS-AD.

Figure 18.5 Comparison analysis of support on DNN and TCA-RS-AD.

Figure 18.6 Comparison analysis of Metrics for Error Rate & Accuracy.

Figure 18.7 Accuracy rating for DNN and TCS-RS-AD.

Figure 18.8 Comparison of error ratings for DNN and TCA-RS-Ad Models.

Chapter 19

Figure 19.1 NSL framework.

Figure 19.2 Bidirectional LSTM.

Figure 19.3 FNN model.

Figure 19.4 Loss & accuracy of base model.

Figure 19.5 Loss & accuracy of graph regularization.

Figure 19.6 Loss & accuracy of BiLSTM model.

Chapter 20

Figure 20.1 Basic concept of a recommender system.

Figure 20.2 Types of recommender system.

Figure 20.3 Machine learning algorithms.

Figure 20.4 Structure of the similarity tree.

Figure 20.5 Similarity tree for a given active query farmer.

Figure 20.6 Proposed model.

Figure 20.7 Graph for PPV & FDR.

Figure 20.8 Graph for precision & accuracy.

Chapter 21

Figure 21.1 Diagram of dataset collection process.

Figure 21.2 Snapshot of a tweet data crawled from Twitter.

Figure 21.3 Diagram of data preprocessing process.

Figure 21.4 Diagram of influence and activities analysis process.

Figure 21.5 Diagram of recommendation process.

Guide

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Machine Learning in Biomedical Science and Healthcare Informatics

Series Editors: Vishal Jain and Jyotir Moy Chatterjee

In this series, the focus centers on the various applications of machine learning in the biomedical engineering and healthcare fields, with a special emphasis on the most representative machine learning techniques, namely deep learning-based approaches. Machine learning tasks are typically classified into two broad categories depending on whether there is a learning “label” or “feedback” available to a learning system: supervised learning and unsupervised learning. This series also introduces various types of machine learning tasks in the biomedical engineering field from classification (supervised learning) to clustering (unsupervised learning). The objective of the series is to compile all aspects of biomedical science and healthcare informatics, from fundamental principles to current advanced concepts. Submission to the series: Please send book proposals to [email protected] and/or [email protected]

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Recommender System with Machine Learning and Artificial Intelligence

Practical Tools and Applications in Medical, Agricultural and Other Industries

Edited by

Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Sarika Jain, Ahmed A. Elngar and Priya Gupta

This edition first published 2020 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2020 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

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For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-71157-5

Cover image: Pixabay.ComCover design by Russell Richardson

To our Parents & Well Wishers

Preface

This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in specific domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. In industry point of view, for an individual item or product recommendation system can help to developed for better selling.

Chapter 1 discusses about pros and cons of method like cold-start, scal-ability, sparsity is explained in detail in terms of recommender systems. Various other approaches of recommendation systems are explained like multi-criteria-based recommender systems, risk-aware recommender systems, mobile recommender system, hybrid recommender system, healthcare recommender system, etc.

Chapter 2 provides an insight into the implementation of the recommender system in both tangible and non-tangible products as well as the service care industry.

Chapter 3 discusses both of data exchange and extraction processes with respect to Tourism Information System. Authors described about the importance of these processes and review how these are being dealt by researchers currently.

Chapter 4 deals with different concepts and challenges of recommendation systems, and how artificial intelligence and machine learning can be used for them. The chapter mainly focuses on the concepts and techniques used by the recommendation system for better suggestion.

Chapter 5 provides a recommender system based on a machine learning approach may be developed which could suggest the type of crop and the fertilizer may be used to increase their productivity and consequently, their income.

Chapter 6 proposes Restrictive Boltzmann Machine Approach (RBM) and hybrid deep learning method i.e. RBM with Convolutional neural network (CNN) (CRBM).

Chapter 7 proposed “Machine Learning-based Recommender System for Breast Cancer Prediction (MLRS-BC)” aims to provide an accurate recommendation for breast cancer prognosis, through four distinct phases namely: Data collection; Preprocessing; Training, testing and validation; and Prediction/Recommender.

Chapter 8 deliberates the concepts of content-based recommender systems by including different aspects in their design and implementation.

Chapter 9 discuss about the various methods to recommend item based on contents.

Chapter 10 The Content-Based Health Recommender System and associated popular Machine Learning algorithms are discussed in this chapter, including their usefulness to enhance profile health records (PHR) solutions.

Chapter 11 proposed context attributes along with the services provided by the social media for achieving better recommendation called as context-based social media recommendation system.

Chapter 12 provides analysis based on the challenge that Netflix offered to the data science community. The objective in this analysis is to train multiple machine learning algorithms using inputs from one data set to predict movie ratings in another data set.

Chapter 13 provides different types of products/items-based recommendations systems have been explained.

Chapter 14 introduced a trust-based recommender system is built to make a detailed review on the applicability of proposed system, where trust is the key in decision making process.

Chapter 15 introduced a Recommender System HealthMudra Using Blockchain for Prevention of Diabetes in details.

In Chapter 16 various health care recommender systems have been discussed. Different technologies to design recommender system are explained.

Chapter 17 describes about a new composite and comprehensive recommender system named Temporal Change Analysis-based Recommender System for Alzheimer Disease Classification (TCA-RS-AD) using deep learning model.

Chapter 18 provides an effective model has been discussed in egocentric OSN by incorporating an efficient influence measured Recommendation System in order to generate a list of top most influenceable target users among all connected network members for any specific social network user.

Chapter 19 aims at developing a recommender system based on tree data structure for farmers. The proposed system recommends seeds, fertilizers, pesticides and instruments based on farming and farmers’ location preferences when buying seeds online.

Chapter 20 describes a new composite and comprehensive recommender system named Temporal Change Analysis-based Recommender System for Alzheimer Disease Classification (TCA-RS-AD) using a deep learning model.

Chapter 21 describes the case study on “Crop Disease Detection and Yield prediction”. The study includes identification of crop condition, disease detection, prediction about production of specific crop and recommendation. The various machine learning techniques or algorithms can be used to monitor, disease detection and predict appropriate crop cultivation.

We like to thank all the authors for their valuable contribution which make this book possible. Among those who have influenced this project are our family and friends, who have sacrificed a lot of their time and attention to ensure that we remained motivated throughout the time devoted to the completion of this crucial book.

The EditorsMay 2020

Acknowledgment

I would like to acknowledge the most important people in my life, my father Aloke Moy Chatterjee, my uncle Mr. Moni Moy Chatterjee & my late mother Nomita Chatterjee. This book has been my long-cherished dream which would not have been turned into reality without the support and love of these amazing people. They have continuously encouraged me despite my failing to give them the proper time and attention. I am also grateful to my friends, who have encouraged and blessed this work with their unconditional love and patience.

Jyotir Moy ChatterjeeDepartment of ITLord Buddha Education Foundation (APUTI)KathmanduNepal-44600

Part IINTRODUCTION TO RECOMMENDER SYSTEMS

1An Introduction to Basic Concepts on Recommender Systems

Pooja Rana, Nishi Jain and Usha Mittal*

Department of Computer Science and Engineering, Lovely Professional University, Phagwara, India

Abstract

In today’s world, we find a wide range of possibilities of any search that we do online and we might find difficulties in choosing what we actually need. To address these issues, recommendation System plays a major role. A recommender system is a filtering system that filters the data using different algorithms and recommends the most relevant data to the user. For instance, a recommender system for e-commerce requires a past history of the site and if the user is not having any past history then the recommender system recommends the bestselling product or most popular product present in the market. Recommendation systems are effective tools for personalization, are always up-to-date, and gives a recommendation based on actual user behavior. Besides being useful in buying products it has a few disadvantages like it is difficult to set up and get running as they are database-driven. Sometimes recommendations are wrong which makes customers unsatisfied. Recommender system is used in different areas like recommendation for entertainment such as movies, songs etc., e-learning web site recommendation, newspaper recommendation and e-mail filters.

In this chapter, various recommendation techniques with their pros and cons and different evaluation metrices has been discussed.

Keywords: Recommendation, item-based, rating, artificial intelligence

1.1 Introduction

A recommender system is a sub-category of an information extraction system that helps to find the ranking or user preference for a particular item. Recommendation systems are dedicated software and methods that give ideas related to things that are used by different users [1, 2]. Many decisions can be made by considering recommendations like which product to purchase, type of music to listen, or what and where to read online news.

The things that are suggested by the system are known as “Item”. A recommender system generally concentrates on a particular form of item like DVDs, or articles and thus its proposal, its graphical user interface (GUI), and the primary method used to make the suggestions are adapted to give beneficial and real recommendations for a particular form of item.

Consider an example of toy recommendation system that assists customers to select a toy to buy. The popular e-commerce web site i.e. Amazon.com also uses a recommendation system to identify the online store for every user [3]. As suggestions and choices are generally personalized, different users or user groups get different suggestions. In case of magazines and newspapers, non-personalized suggestions are produced, which are very easy to generate. Consider the example of selecting best ten books or CDs.

To make the personalized suggestions, ranked lists of items are produced. User’s preferences and constrains are considered for generating the ranking to extract the most suitable products and services. For computing the most similar products and services, user’s preferences are collected implicitly or explicitly by understanding the users’ actions.

The fast development and diverse data existing on the web and existence of new e-business services like purchasing goods, comparing items, auction, etc. often stunned customers, leading them to make wrong choices. Thus, rather than giving benefit to customers, it starts decreasing the well-being. Actually, choice, with its insinuations of liberty, independence, and self-determination can become dangerous; generating a sense that independence may come to be observed as a kind of misery-inducing dictatorship [4].

These days, use of recommender systems has widely increased, indicated by the following facts:

In high-rated internet sites, recommendation systems have a vital role like Yahoo, Amazon.com, Netflix, YouTube, TripAdvisor and IMDB. Now, RSs as a part of service have been provided to the subscriber by many media companies. For instance, Netflix, the online movie rental service, has paid a great price i.e. one million dollars as prize to the group that first successfully improved the accuracy of its recommendation system [

3

].

ACM Recommender Systems (RecSys) has been established in 2007 dedicated for conferences and workshops.

1.2 Functions of Recommendation Systems

Recommender system offers suggestions to the user about a particular item that user wants to use. Now this definition can be refined by representing the different roles that a system can play. Recommender system plays different roles according to the user for example; a recommender system used by travel intermediary is usually used to increase the revenue like Expedia.com and Visitfinland.com while the customer’s objective for using the systems is to find an appropriate hotel and interesting events/attractions when visiting a destination.

The following are different reasons to exploit RS technology by service providers:

Increase the sale of product: The major objective of a commercial RS is to increase its sale, or in other words to sell those products also which can’t be sold without recommendations. Recommendations are provided considering that suggested products and services meet the customer’s requirements. Non-commercial recommendations are used for similar objectives. Consider an example of a content writer who wants to increase the number of news reader on his site. The goal of the service provider to use the recommender system is to increase the users that opts the products or services as compared to users surf the site.

Selling variety of products: RS also help a user to find items that might be difficult to find without a particular reference. For example, the recommender system used in Netflix has the goal of renting maximum movies in the list, rather than the most popular movies. Making such recommendations could be hard without a recommender system because the service provider cannot take the risk of suggesting videos which do not meet the user’s taste. In this way, the recommender system also suggests movies which are not even popular.

User satisfaction. The recommender system helps in improving the experience of the person with the application or web site. It provides interesting, significant and, relevant recommendations as well as provides better human– computer interaction. The effective recommendations i.e. accurate as well as interactive user interface increases usage of the system and the chances that the suggestions will be acknowledged.

User loyalty. A customer always prefers to use a web site or application which identifies its old users and treats him as a respected/valuable customer. It is a common feature of a recommender system as it computes recommendations/suggestions, considering the data attained from the user in earlier interactions such as his ratings of products. Therefore, the more the customer uses a particular site, the better his model becomes, i.e., output will be more customized to user’s preferences.

Better understand of user needs: The recommender system is acting as an active learner to user’s preferences by collecting explicitly or predictions made by the system. The business holders may then re-use this information for improving the stock management or production of items.

1.3 Data and Knowledge Sources

Recommender systems are knowledge extraction systems that actively collect different types of information to make the suggestions. Facts are mostly related to things to be recommended and the consumers who will get such suggestions. Available data sources are very large and diverse, their use for making recommendations are largely depends upon the recommendation techniques to be used.

Items: Products and services that a recommender system recommends are referred to as items. The recommendation of an item is considered positive if the suggested product is beneficial for the consumer. If the product is not meeting user requirements and the customer took a wrong decision while choosing it, then recommendation is negative.

For example, a news recommender system designer must consider structure of news, the textual representation, and the time significance of any news. As while reading news, no monetary cost is associated but cognitive cost is there. If the system makes a positive recommendation, then cost of searching and reading news is dominated by the benefit of getting relevant and valuable knowledge. But if recommendation is negative, then the user’s time is wasted which restricts the user to use the system again. In other areas, like mobile phones, or business investments, actual monetary cost is associated which becomes significant component to take into account while choosing the most suitable recommendation techniques.

Examples of items having low difficulty and value are news, web articles, e-books, DVDs, and movies. Items having high complexity and value are laptops, LCDs, mobile phones, digital cameras, electrical appliances, PCs, etc. Insurance policies, travel plans, financial investments and jobs are considered as most complex items [

10

]. According to the basic recommendation approach, recommender systems use a variety of properties and characteristics of the products and services. For instance, in a movie recommendation, the genre like comedy, thriller, etc., as well as the actors, and directors can be used to define a movie.

Users: Users of a recommender system may have very diverse aims and features. In order to make positive recommendations, the system should exploit a variety of data about the users. This data can be organized in several means and again the usage of data depends upon the recommendation approach.

Transactions: Any recorded communication between a recommender system and user is referred to as transaction. These are logs that collect essential information generated at the time of interaction and these are valuable for the recommendation technique. The log may also have an explicit feedback given by the user like ranking to the particular product.

Ratings are the most common method of transaction data that a recommender system gathers. These rankings may be collected explicitly or implicitly. There different types of ratings as follows:

Ranking can be a numerical value like 1–5 given in the items related with e-commerce sites like Amazon.com.

Ranking can be an ordinal value like “strongly agree, agree, neutral, disagree, strongly disagree” normally used in surveys.

Ranking can be a binary value in which feedback is taken from user as product is useful or not

.

1.4 Types of Recommendation Systems

Different types of recommendation system are available that differ in terms of problem domain, Information used, and importantly recommendation algorithm used to make prediction. There are mainly two types of recommendation systems as shown in Figure 1.1 i.e. content-based RS and Collaborative filtering methods.

1.4.1 Content-Based

Based on the previous responses submitted by the user, the system learns to make recommendations by analyzing the feature similarity among items. For example, based upon the rating of a user for different genre of movies, the system will learn to recommend the genre which is positively rated by the user. A content-based recommendation system builds a user profile based upon the previously rated items by the user. A user profile represents the user interests and is able to adapt to new interest also. Matching of user profile against the features of content object is basically the recommendation process. Result of this process is a judgment that signifies the user interest in the object. A highly accurate profile of user interests will result in usefulness of an information access process. For instance, it might be used to filter the web results by determining whether a user is interested in the specific page or not.

Figure 1.1 Different recommendation techniques.

The recommendation process comprises of 3 steps, each of which is handled separately.

Content Analyzer:

If data is non-structured, some pre-processing is required in order to obtain relevant information. The main responsibility of a content analyzer is to represent the contents coming from the source in a relevant form for the next processing steps. Feature extraction techniques are used to modify item structure from original to the targeted (e.g. web pages represented as keyword vectors). This representation is input to the next component.

Learner:

This module constructs the user profile by generalizing the data obtained from the previous component. Machine learning techniques are used to learn the generalize strategy, which are able to construct a model based upon user preferences in the past, both positive and negative. For example, profile learner of a web page recommendation system will implement a relevance feedback method which combines positive and negative feedback into a prototype vector representing the user profile.

Filtering Component:

This module makes use of the user profile to derive related items. This is done by matching the profile alongside items to be recommended. Based upon the similarity metrics, a relevant judgment is produced either binary or continuous.

Content Recommendation systems acquire the recommendation idea from the past data of a user based on what items a user has purchased or liked. Both user and item attributes are of equal importance in terms of making a prediction. Consider the example of news recommender, features like categories (Finance, Sports, Health, Technology, Politics, Entertainment, Automobile, etc.) or location (local, national or international) etc. are required to find the similarity index between news. To extract features like sentiment score, TF-IDF scores are used. In this approach, the profile of each user as well as each item is created and two vectors are created.

Item vector:

A vector of length N and contains value 1 for words having high TF-IDF and 0 otherwise.

User vector:

A 1 × N vector containing probability of occurrence of word of every word in the article. The user vector is based in the attributes of item.

After that, similarity between user and article is computed using following methods:

Cosine similarity:

It is used to measure similarity between user and item. This way gives user–item similarity. This method is best when we have high dimensional features especially in information retrieval and text mining. The range of this is between −1 and 1 and there are two approaches:

Top-n approach: According to this, top n best products are recommended and value of ‘n’ is decided by user [

5

].

Rating scale approach: In this, a prefixed threshold is fixed and all the items having value greater than threshold are suggested as given in

Equation 1

.

(1)

Jaccard Similarity:

This similarity is computed using

Equation 2

. This method is used to compute item–item similarity. It compares item vectors with each other and returns the most similar item. This is only useful with binary vectors. If any ratings or rankings having multiple values then this method is not applicable.

(2)

Euclidean Distance:

It is computed using the formula given in

Equation 3

.

(3)

Pearson’s Correlation:

It is computed using the formula given in

Equation 4

. It tells the correlation between the two items. Higher correlation means higher similarity.

(4)

1.4.1.1 Advantages of Content-Based Recommendation

User Independence—

Content-Based Recommendation system build a user profile only based upon the rating or purchased done by the user in the past. No neighbor is considered for building the profile of the user who has same interest as of user.

Transparency—

Explanation facility of content-based recommendation system is transparent to the user which means it provides explanations of the recommendations.

New Item—

It does not suffer from the first rater problem which means if an item is not rated by any user it is still able to recommend that item to the user.

1.4.1.2 Disadvantages of Content-Based Recommendation

Limited content analysis—

One of the shortcomings of content-based recommendation system is limited content associated with the item in terms of number of features and type of features. Domain Knowledge is also crucial to make a recommendation. For example, making a movie recommendation system requires knowledge of actors and directors of the movie. Proper differentiation cannot be done between the item’s user likes and items user dislikes if available data is insufficient. Representation sometimes is able to capture only certain aspects of user choice but not all. For example, Web pages, feature extraction techniques from text completely overlook visual qualities and additional multimedia information.

Over-specialization—

Content-based recommendation system does not have any essential method to explore something unpredicted. System can recommend only those items which result in high score while matching with the user profile. It is also called serendipity problem which shows the limit of recommendations that can be made by content based. A “perfect” content-based technique would hardly provide anything new, limiting the range of applications for which it would be beneficial.

New user—

To make recommendation system to learn about user preferences, sufficient ratings need to be collected. System is not able to provide reliable recommendations to the new users as no past data is available.

1.4.2 Collaborative Filtering

This approach uses ‘user behavior’ for recommendations. In this approach, there is no feature corresponding to users or items. It uses a utility matrix and most commonly used in industries as it is independent from any additional information.

Limitation of content-based recommendation system can be overcome by the collaborative approach for instance it can make prediction for those items for which content is not available. It uses the feedback of other users to recommend such items. These systems evaluate the quality of an item based on peer review. It can also suggest products with different content as long as other users have shown the interest in the content.

There are 2 categories of collaborative filtering as shown in Figure 1.2:

Memory-based (neighborhood) approach:

In this, utility matrix is learnt and suggestions are given by asking the given user with rest of the utility matrix [

9

]. Let’s suppose we have ‘m’ movies and ‘u’ users. To find out how much user likes movie ‘k’

Equation 5

is used:

(5)

Figure 1.2 Different techniques of collaborative filtering.

The above formula will give the average ranking that customer ‘i’ has specified to all the items. Rating of product can be estimated as given in Equation 6:

(6)

It is easy to compute, but if data becomes sparse, performance becomes poor. Now the similarity between users ‘a’ and ‘i’ can be calculated using the methods like cosine similarity, Pearson’s correlation, etc.

Memory-Based Collaborative Filtering is further divided into two categories i.e. user-based filtering and item-based filtering [7].

User-Item filtering: In this method, for a new item ‘i’ for a particular user ‘u’ rankings of nearest neighbors of user ‘u’ are used to compute the ranking rui of the user ‘u’ but only those neighbors are considered who have already given a ranking for the item ‘i’. The rating rui can be estimated as shown in

Equation 7

.

(7)

Above equation does not consider the different level of similarity can occur among neighbors.

The prediction of item is calculated by computing the weighted sum of the user ratings of ‘user-neighbors’ given by other users to item ‘i’. The prediction is given by the formula in Equation 8.

(8)

Where Pu,i is the prediction of an item, Rv,i is the rating given by a user ‘v’ to an item  and Su,v is the similarity between users.

What will happen if a new user or new item is inserted in the dataset? There is term known as cold start which is of two types:

Visitor cold start:

When a new consumer is presented to the knowledgebase, as system is not having any past data of the user, it becomes difficult to suggest any product to him. To resolve this issue, overall or regionally most popular products are recommended.

Product cold start:

When a new item comes in the market or given to the system, user’ actions are required to decide its value. Higher the reviews and ratings product got from users, the easier it becomes for the system to suggest to appropriate consumer.

1.5 Item-Based Recommendation vs. User-Based Recommendation System

Five points need to be considered in order to make a choice between user-based and item-based neighborhood recommendation system. Points are as follow:

Accuracy

: Ratio of users and items is typically responsible for the accuracy of neighbourhood recommendation system. In user-based recommendation system, similarity between two users is calculated by analysing the scores given by the users for the same item. Item-based approaches compute the similarity between two items by analyzing the scores given by the same user.

Efficiency

: Efficiency in terms of memory and computational power also depends upon ratio of users and items. So, if the users are more than items that happen in most of the cases, item-based recommendation systems are more reliable in terms of memory and time required to calculate the similarity. On the other hand, time complexity is same for both because it depends upon the number of users and number of items.

Stability

: Stability of user-based and item-based system is related to occurrence and change in number of users and items in the system. If items are static then we should use item-based recommendation system because similarity weights of items can be computed at irregular time intervals. On the opposite hand, if the list of items is changing then user-based system are most preferable.

Justifiability

: Based upon preferences, whether justification is required or not, item-based or user-based recommendation system is selected. Item-based methods can simply be used to explain why a recommendation is made. As an explanation to the user a list of neighbor items and their similarity weights can be shown to the user which are used for making the recommendation. User can also participate in the process by modifying the neighbours. On the other hand, user-based methods can’t explain the recommendation process because the user is not aware about other’s preferences.

Serendipity

: Problem of serendipity occurs in item-based recommendation system because it recommends only those items to the users which have been liked by the user in the past. For example, in the movie recommendation system only that movies will be recommended to the user whose genre or actors are same as of previously liked. On the contrary, user based can make unexpected recommendation by analysing the neighbors who have made same rating to the item as the user and checks the ratings on different items by the neighbor user which are yet not rated by the user.

1.5.1 Advantages of Memory-Based Collaborative Filtering

Easiness

: Neighborhood-based approaches are instinctual as well as easy to implement. Only one parameter requires tuning i.e. how many neighbors to be considered for final evaluation.

Justifiability

: Memory based collaborative filtering approaches also offer a crisp explanation for the calculated results. Item-based methods can simply be used to explain why a recommendation is made. As an explanation to the user a list of neighbor items and their similarity weights can be shown to the user which are used for making the recommendation. User can also participate in the process by modifying the neighbours.

Efficiency

: In terms of efficiency, neighborhood-based system is more preferable. As compared to others, model-based systems do not require expensive training phases as well as memory consumption is low.

Stability

: Memory based collaborative systems are slightly affected by the continuous insertion of consumers, products and rankings. For example, once the similarity between items has been calculated, model can generate suggestions. Once new ranking is added, only similarities between the items need to be computed.

1.5.2 Shortcomings

These systems are fully dependent on the rankings provided by the users.

These systems are not able to handle the sparse data which result in performance degradation.

Recommendation cannot be made for new customers and new products.

System is not scalable.

Model based approach:

This model represents users and items using utility matrix i.e. utility matrix is decomposed into A and B matrix where A signifies user and B denotes the items [

9

]. For matrix decomposition, different techniques like SVD, PCA are used. Rating of each item is computed and product with highest rating is recommended. This model is beneficial when available data is large in volume.

This approach is further divided into 3 sub-types:

Clustering based algorithm.

Matrix factorization-based algorithm.

Deep learning/Neural Nets.

1.5.3 Advantages of Model-Based Collaborative Filtering

It helps to resolve the problem of sparsity and scalability.

Prediction accuracy is better.

1.5.4 Shortcomings

Implementation cost is high.

There is trade-off between scalability and efficiency of model.

Due to dimensionality reduction methods, it may loss valuable information.

1.5.5 Hybrid Recommendation System

In this, two or more techmiques are combined like content-based and collaborative fltering. Limitations of one techniques can be overcommed by other technique. There are several classes of Hybrid Recommendation Systems:

Mixed—In this, number of different techniques are combined together to design a system. Here the generated item lists of each technique are added to produce a final list of recommended items. Recommendations made by all techniques are combined and final result is presented to user.

Weighted—In these, weighted linear functions are used to compute the rank of products by aggregating the output ranks of all systems. P- Tango is the first weighted hybrid system for online newspaper recommender system.

Cascade—These systems work in stages. First method is used to make a rough rating of products and then generated list is refined by second method. In these systems, order of processes matters a lot.

Feature augmentation—In feature augmentation systems, output of one system acts as input to other system. These are also order-sensitive.

Switching—In this, system switches among various recommending methods depending on some conditions. For instance, a CF-CBF method may shift to the content-based recommender if collaborative filtering method doesn’t offer sufficient reliable results.

1.5.6 Advantages of Hybrid Recommendation Systems

Overcome the limitations of collaborative filtering, content-based and other systems.

Recommendation results have been improved.

Can work on sparse data also.

1.5.7 Shortcomings

Expense have been increased.

Complexity has been increased.

External information is required which is not always available.

1.5.8 Other Recommendation Systems

Demographic Filtering:

These systems use demographic data like age, gender, education, etc. for classifying groups of users. New user problem does not exist in such systems as they do not consider ratings for making recommendations. Though, it is hard today to gather sufficient demographic information due to online privacy concerns. These can be used with other systems as a hybrid model for better results.

Knowledge-Based Filtering:

These systems use knowledge about users and their requirements/preferences to make suggestions [

4

]. Constraint-based systems belong to knowledge-based systems which recommend products that are seldom bought like car, house, etc.

1.6 Evaluation Metrics for Recommendation Engines

To evaluate the recommender system, different metrics are used like mean absolute error, root mean square error, precision, recall, F1 score, etc. [6, 8].

Recall:

It is defined as percentage of products actually user liked and products actually suggested.

(9)

Here, in Equation 9tp refers to the number of products suggested by RS to a user and tp + fn refers to the total products liked by users. Higher the value of recall better is the recommendation result.

Precision:

It is defined as total number of products actually liked by user from all the products recommended by system.

(10)

Here, in Equation 10tp refers to the number of products suggested by RS to a user and tp + fn refers to total products suggested. Higher the precision better is recommendation.

Root Mean Squared Error (RMSE):

It is used to measure the error in the predicted values.

(11)

Here, in Equation 11 ‘Predicted’ is the value given by the model and ‘Actual’ is the original value. Lower the RMSE value better is the recommendation.

Mean Absolute Error (MAE):

It is used to compute the difference between actual and predicted value as given in

Equation 12

.

(12)

The smaller the MAE value better is the recommendation.

The below mentioned metrics considers the order of the product recommended so they are the ranking metrics:

MAP at k (Mean Average Precision at cutoff k): It is computed by selecting the subset of suggestions given by RS from rank 1 to rank k as given in Equation 13.

(13)

Higher the MAP value better is the recommendation.

1.7 Problems with Recommendation Systems and Possible Solutions

Cold start problem:

When a new customer uses the system or new products are added to it, then this problem arises. The reason behind the problem is neither the system is able to predict the taste of the new users nor system contains the ratings of new products leading to unacceptable results. This issue can be resolved in many ways: the sleepers, different methods can be used like item popularity, linked open data, entropy and content-based methods.

By taking the ranking of some products from the customer at the beginning.

By taking the users preferences and requirements explicitly.

Recommending products to the customer depending on the gathered demographic information.

User demographic information can be used to identify about the place, zip-code along with interactions of the new user with the system so that recommendations can be made on the basis of rankings given by other customers having similar demographic information. The products which are useful and good but are not rated yet are known as sleepers. To handles

Synonym:

Two or more different words which represents to same object or meaning is known as synonym. But recommendation systems are not able to differentiate these words. For example, “comedy movie” and “comedy film” both words are considered different by a memory-based CF approach. Excessive use of synonyms reduces the performance of recommender system. To remove the synonym problems, many techniques like ontologies, Latent Semantic Indexing (LSI) and Single Value Decomposition (SVD) could be used.

Shilling Attacks:

If a malicious user or competitor provides untruthful rankings to some products either to increase product visibility or to reduce the popularity, this type of attack is known as shilling attack. These attacks reduce the performance and quality of recommender system as well as break the faith of customers. CF based techniques are more prone to this threat as compared to the item-based CF approach. Bandwagon, average, random are different recommender systems attacks. To detect the attacks, various methods such as generic, hit ratio, model specific attributes and prediction shift are used. Different parameters to categorize the attack are aim of attack, size of attack, and prerequisite data to initialize the attack.

Privacy:

Providing personal data to the recommender systems may increase the system performance but may lead to problems of data privacy and security. Users are reluctant to feed data into recommender systems that suffer from data privacy issues. Therefore, a recommender system, whether CB or CF, should build trust among their users, however CF recommenders are more prone to such privacy issues. In CF technique, user data including ratings are stored in a centralized repository which can be compromised resulting in data misuse. For this purpose, cryptographic mechanisms can be used by providing personalized recommendations without involving third parties and peer users. Other techniques include using randomized perturbation techniques, allowing users to publish their private data without exposing their identities, and using Semantic Web technologies especially ontologies in combination with NLP techniques to mitigate the unwanted exposure of information. Limited Content Analysis and Overspecialization: Content-based recommenders rely on content about items and users to be processed by information retrieval techniques. The limited availability of content leads to problems including overspecialization. Here, items are represented by their subjective attributes, where selecting an item is based mostly on their subjective attributes. Features that represent user preferences in a better way are not taken into account. For many domains, content is either scarce such as books or it is challenging to obtain and represent the content such as movies. In such cases relevant items cannot be recommended unless the analyzed content contains enough information to be used in distinguishing items liked/disliked by the user. This also leads to representation of two different items with same set of features, where, e.g., well-written research articles can be difficult to distinguish from bad ones if both are represented with same set of keywords. Limited content analysis leads to overspecialization in which CB recommenders recommend items that are closely related to user profile and do not suggest novel items. In order to recommend novel and serendipitous items along with familiar items, we need to introduce additional hacks and note of randomness, which can be achieved by using genetic algorithms that brings diversity to recommendations being made. The problem is relatively small in CF recommenders where unexpected and novel items may get recommended.

Grey Sheep: