Please cite this article as: S. Gupta and V. Kant, Credibility score based multi-criteria recommender system, Knowledge-Based Systems (2020) 105756, https://doi.org/10.1016/j.knosys.2020.105756. Knowledge-Based Systems xxx (xxxx) xxx Contents lists available at ScienceDirect Knowledge-Based Systems journal homepage: www.elsevier.com/locate/knosys Credibility score based multi-criteria recommender system ✩ Shweta Gupta, Vibhor Kant ∗ The LNM Institute of Information Technology, Jaipur, Rajasthan, 302031, India article info Article history: Received 24 April 2019 Received in revised form 9 February 2020 Accepted 8 March 2020 Available online xxxx Keywords: Credibility Collaborative filtering Genetic algorithm Multi-criteria ratings Recommender system abstract Recommender system has been emerged as a personalization tool to solve the issue of information overload in an e-commerce environment. Traditional collaborative filtering (CF) based recommender systems (RSs) suggest items to users based on their overall ratings which are used to find out similar users. Multi-criteria ratings are used to capture user preferences efficiently in multi-criteria recommender systems (MCRS), and incorporation of various criteria ratings can lead to higher performance in MCRS. Usually, user relies on the credibility of an item provided through his/her social circle or similar users, which is called a personal view on items from their close ones. However, it is not generally sufficient to depend exclusively on the personal view of the user. Therefore, public view that includes whole community can play a key role in the credibility of an item. In this paper, we propose a MCRS based on the credibility score of an item, which is an aggregated value of credibility scores on various criteria of an item. These credibility scores are computed based on personal and public views. However, different users have different priorities to various criteria of an item. Therefore, we use genetic algorithm (GA) to learn appropriate weights in the aggregation task of credibility score. The experiment results on Yahoo! Movies and modified MovieLens dataset demonstrate the effectiveness of proposed credibility score based MCRS in terms of coverage, recall, precision, and f-measure. © 2020 Published by Elsevier B.V. 1. Introduction The rapid expansion of Web has brought huge convenience for users as well as causing a problem of information overload, which makes it problematic for users to find useful information according to their requirements [1,2]. RSs are intelligent tools that help to solve this overload problem by suggesting the relevant information to users [1,3]. With the intense growth of the Inter- net, RS has been deployed to a variety of online systems [4], such as online videos (YouTube), movies (Netflix), songs (Last.FM), books (Amazon), online social networks (Facebook), news articles (Globo.com) [5], hotels (Goibibo) [6] etc. Generally, RS can be built based on various filtering techniques such as content-based filtering (CBF), collaborative filtering (CF) and hybrid filtering [1,2]. CBF technique [7], recommends items to users based on their past preferences and the contents of preferred items [8], while collaborative filtering (CF) generates recommendations to users based on similar users. CF, the most prevalent technique in the area of RS, has successfully explored ✩ No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work. For full disclosure statements refer to https://doi.org/10.1016/j.knosys. 2020.105756. ∗ Corresponding author. E-mail addresses: shweta7100@gmail.com (S. Gupta), vibhor.kant@gmail.com (V. Kant). in various domains such as movies, music, news, etc. The CF technique has been categorized into two categories- model-based filtering and memory-based filtering. In these two categories, memory-based CF provides more accurate recommendations to users [7]. These memory-based techniques are further classified into two categories such as user-based CF and item-based CF. In user-based CF, items are recommended based on similar users directly, while item-based CF generates recommendations based on similar items, however, these similarities are computed based on common users [4,9]. Among those two techniques, CF is used more frequently to build RSs. However, each filtering technique has its own pros and cons. Therefore, hybrid filtering [10,11] came into existence in building efficient RS by overcoming their weaknesses. There are two major extensions of RS i.e. group RS and multi- criteria RS. Standard recommendation approaches used in various domains focus mostly on a single user. But there are plenty of situations where recommendations are needed for a group of users. For such scenarios, the group recommendation is the optimal solution [12]. In RSs, users provide their ratings to experi- enced items for describing their preferences. Majority of existing RSs use these overall ratings to generate recommendations to users. These overall ratings are not sufficient to capture users preferences efficiently because users may like/dislike any item based on some specific attributes [13]. For example, user u 1 has given an overall rating 5 to a particular movie based on its https://doi.org/10.1016/j.knosys.2020.105756 0950-7051/© 2020 Published by Elsevier B.V.