Analysis and Classification of Multi-Criteria Recommender Systems

Abstract

Recent studies have indicated that the application of Multi-Criteria Decision Making (MCDM) methods in recommender systems has yet to be systematically explored. This observation partially contradicts with the fact that in related literature, there exist several contributions describing recommender systems that engage some MCDM method. Such systems, which we refer to as multi-criteria recommender systems, have early demonstrated the potential of applying MCDM methods to facilitate recommendation, in numerous application domains. On the other hand, a comprehensive analysis of existing systems would facilitate their understanding and development. Towards this direction, this paper identifies a set of dimensions that distinguish, describe and categorize multi-criteria recommender systems, based on existing taxonomies and categorizations. These dimensions are integrated into an overall framework that is used for the analysis and classification of a sample of existing multi-criteria recommender systems. The results provide a comprehensive overview of the ways current multi-criteria recommender systems support the decision of online users.

Trust-based Modelling of Multi-criteria Crowdsourced Data

“…This related work contemplates: (1) multi-criteria tourism crowdsourced ratings in hotel recommendation systems; (2) collaborative filtering; and (3) trust-based modelling. Adomavicius and Kwon [2], Bilge and Kaleli [4], Lee and Teng [24], Jhalani et al [17], Liu et al [26], Manouselis and Costopoulou [27] and Shambour et al [32] have explored the integration of multi-criteria ratings in the user profile, mainly using multimedia datasets to validate their proposals. Davoudi et al [7], Jia et al [18] and Zhang et al [37] have explored the trust modelling for rating prediction presenting trust models together with matrix factorisation algorithms or similarity metrics.…”

Recommender Systems for Learning

“…Even though this definition covers also the classic text-based filtering systems, Burke (2002) states that two criteria distinguish recommender systems from text-based ones: the criterion of 'individualised' and the criterion of 'interesting and useful' content. Table 1.1 provides an overview of relevant definitions that we have identified in the literature, extending the initial collection reported in Manouselis and Costopoulou (2007).…”

Towards a Social Trust-Aware Recommender for Teachers

“…CF methods are often categorized according to type or technique. Type refers to memory-based and model-based algorithms (Manouselis and Costopoulou 2007;Schafer et al 2007). Model-based algorithms use probabilistic approaches to develop a model of a user from the user's history and profile.…”

“…However, each of these reviews focuses only on some of the dimensions to classify recommender systems and none of them present an integrated framework for the classification of recommender systems ). Manouselis and Costopoulou (2007) propose a framework for categorizing the dimensions of recommender systems, which were identified in the related studies. We will use this framework to investigate the characteristics that should be considered when designing a recommender system for teachers.…”