Recommendation System using Python E-Book

Utpal shrivastava

• Herausgeber: BookRix
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Veröffentlichungsjahr: 2020

Beschreibung

In the very recent years, development of recommendation system has been a more heated problem due to a higher level of data consumption and the advancement of machine learning techniques The book presents an improved algorithm based on machine learning on hybrid approach using collaborative filtering, content based filtering and popularity based filtering using python 

Leseprobe

Utpal Shrivastava, Dr. Vikas Thada

Recommendation System using Python

BookRix GmbH & Co. KG81371 Munich

Chapter 1: Introduction

Chapter -1

Introduction

1.1 Introduction

Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in recommendation system. Recommendation system is categorized in three classes: Collaborative Filtering, Content based and hybrid based Approach. This paper classifies collaborative filtering in two types: Memory based and Model based Recommendation. The paper elaborates these approaches and their techniques with their limitations. This survey shows the road map for research in this area.  Recommendation System is part of Daily life where people rely on knowledge for making decision of their personal interest.

Recommendation system is subclass of information filtering to predict preferences to the items used by or for users. Although there are many approached developed in past but search still goes on due to it’s often usage in many applications, which personalize recommendation and deals with information overload. These demands throw some challenges so different approaches like memory based, model based are used. Recommender system still requires improvement to become better system. Recommendation system is a sharp system that provides idea about item to users that might interest them some examples are amazon.com, movies in movielens, music by last.fm. In this paper, different approached with their techniques are mentioned to compare the limitation of each technique in proper manner to provide proper future recommendations.

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of a recommender system using a machine learning algorithm often has problems and open questions that must be evaluated, so software engineers know where to focus research efforts. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies research opportunities for software engineering research. The study concludes that Bayesian and decision tree algorithms are widely used in recommender systems because of their relative simplicity, and that requirement and design phases of recommender system development appear to offer opportunities for further research.

1.2 Approaches of Recommendation System

Recommendation system is usually classified on rating estimation

Collaborative Filtering systemContent based systemHybrid system

In content-based approach, similar items to the ones the user preferred in past will be recommended to the user while in collaborative filtering, items that similar group people with similar tastes and preferences like will be recommended. In order to overcome the limitations of both approach hybrid systems are proposed that combines both approaches in some manner [15].

1.3 Motivation of the work

Hybrid system provides strong predictive power for recommender systems, and requires the least information at the same time. However, it has a few limitations in some particular situations.

First, the underlying tastes expressed by latent features are actually not interpretable because there is no content-related properties of metadata. For movie example, it doesn’t necessarily to be genre like Sci-Fi in my example. It can be how motivational the soundtrack is, how good the plot is, and so on. Collaborative Filtering is lack of transparency and explainability of this level of information.

On the other hand, Collaborative Filtering is faced with cold start. When a new item coming in, until it has to be rated by substantial number of users, the model is not able to make any personalized recommendations. Similarly, for items from the tail that didn’t get too much data, the model tends to give less weight on them and have popularity bias by recommending more popular items.

It’s usually a good idea to have ensemble algorithms to build a more comprehensive machine learning model such as combining content-based filtering by adding some dimensions of keywords that are explainable, but everyone should always consider the tradeoff between model/computational complexity and the effectiveness of performance improvement.

1.4 Recommender System