Evolutionary Computing Approaches to Multi Criteria Recommender Systems

Abstract

One of the most important challenges facing us nowadays is to customize information based newlineon user preferences. In order to accomplish this objective, recommender system (RS) came newlineinto existence that provides personalized recommendations to users through different decisionmaking newlineprocess based on user s likings and constraints. RS became an important research newlinearea in last two decades and it has been applied successfully in many domains, such as videos newline(YouTube), movies (Netflix), songs (Last.FM), books (Amazon), online social networks (Facebook, newlineTwitter), news articles (Globo.com), hotels (Goibibo) etc. newlineMost of the RSs consider overall ratings to generate recommendations to users. Considering newlineonly overall rating is not sufficient to capture user preferences efficiently because users newlinemay like or dislike any item based on various criteria. Therefore, recommender systems based newlineon overall rating may not perhaps produce quality recommendations to users based on their newlinepreferences. So, instead of considering only overall rating in RS, it would be better to incorporate newlinecriteria ratings while generating recommendations. Hence, multi-criteria recommender newlinesystems (MCRSs) came into existence to provide quality recommendations to users by considering newlinevarious criteria ratings. However, rating data may be more sparse. Over the past decade, newlineMCRS is classified into two categories namely similarity-based approach and model-based approach. newlineIn similarity-based approaches, aggregation of these criteria ratings is an important newlinechallenge for generating recommendations to users. Most of the research in MCRS is focused newlineon estimating users preference but none has focused on item utility. However, item utility newlinecan be a good factor in consideration of quality recommendations to users. In model-based newlineapproaches, elicitation of preference function is a major concern, that can reflect a relationship newlinebetween overall and criteria ratings. newline