Abstract Metrics such as diversity and novelty have become important, beside accuracy, in the design of Recommender Systems (RSs), response increasing users' heterogeneity. Therefore, RSs is now increasingly modelled a multi-objective optimization problem (MOP) for whose solution Multi-objective evolutionary algorithms (MOEAs) been considered. In this paper we focus on k-top recommendation whic...

Recommender engines have become immensely important in recent years because a large number of people depend on internet to browse options out of a vast set of choices. Different websites implement recommender systems using different techniques such as content-based filtering, collaborative filtering or hybrid filtering. Recommender systems face various challenges like scalability problem, cold ...

Trust-aware recommendation approaches are widely used to mitigate the cold-start problem in recommender systems by utilizing trust networks. In this paper, we point out problems of existing trust-aware as follows: (P1) exploiting sparse explicit and distrust relationships; (P2) considering a misleading assumption that user pair having trust/distrust relationship certainly has similar/dissimilar...

Newly emerging location-based and event-based social network services provide us with a new platform to understand users’ preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge to the traditional collaborative filtering-based r...

Virtual representatives are increasingly used in recommender systems to guide users and add conversational aspects. However, the impacts of virtual representatives on users’ evaluations of the recommender system have not been investigated. This study specifically examined the influence of virtual representatives’ anthropomorphism cues on system users’ perceptions of system credibility and likin...

these days, due to growing the e-commerce sites, access to information about items is easier than past. but because of huge amount of information, we need new filtering techniques to find interested information faster and more accurate. therefore recommender systems (rs) introduced for solving this problem. although several recommender approaches have proposed, collaborative filtering (cf) appr...

© Springer International Publishing Switzerland 2015 K. Arai et al. (eds.), Intelligent Systems in Science and Information 2014, Studies in Computational Intelligence 591, DOI 10.1007/978-3-319-14654-6_14 Abstract Social media systems allow users to share resources with the people connected to them. In order to handle the exponential growth of the content in these systems and of the amount of u...

This work focuses on top-k recommendation in domains where underlying data distribution shifts overtime. We propose to learn a time-dependent bias for each item over whatever existing recommendation engine. Such a bias learning process alleviates data sparsity in constructing the engine, and at the same time captures recent trend shift observed in data. We present an alternating optimization fr...