Enhancing Prediction Accuracy of A Multi-Criteria Recommender System Using Adaptive Genetic Algorithm
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Enhancing Prediction Accuracy of A Multi-Criteria Recommender System Using Adaptive Genetic Algorithm

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Abstract

Recommender systems are powerful intelligent systems considered to be the solution to the problems of information overload. They provide users with personalized lists of recommended items, using some machine learning techniques.

Traditionally, existing recommender systems have used single rating techniques to estimate users’ opinions on items.

Because user preferences might depend on the attributes of several items, the efficiency of traditional single-rating recommender systems is considered to be limited, since they cannot account for various items’ attributes.

A multi-criteria recommendation is a new technique that uses ratings of various items’ attributes to make more efficient predictions.

Nevertheless, despite the proven accuracy improvements of multi-criteria recommendation techniques, research needs to be done continuously to establish an efficient way to model criteria ratings.

Introduction

Intelligent systems are systems that require knowledge organisation to interpret, test and analyse acquired information.

Intelligent systems are required in most of our day-to-day activities, such as e-commerce, online-booking, social media, e-shopping and other information-rich environments.

Recommender systems interact with users in a personalized way, obtain information about a user’s tastes or preferences and use this knowledge to make suggestions and provide assistance in situations where users have to make a decision between a wide range of possible options.

In this chapter we endeavour to explain the recommender system and its techniques, introduce multi-criteria recommender systems and also a genetic algorithm. Statement of the problem, aims and objectives, significance and scope of this study will also be introduced in this chapter.

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