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 system
• Content based system
• Hybrid 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

Impressum

Verlag: BookRix GmbH & Co. KG

Tag der Veröffentlichung: 26.05.2020
ISBN: 978-3-7487-4327-9

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