Context-aware recommender systems (CARS) have been developed to adapt to users’ preferences in different contextual situations. Users’ emotions have been demonstrated as one of effective context information in recommender systems. However, there are no work exploring the effect of emotional reactions (or expressions) in the recommendation process. In this paper, we assume that users may give si...

Recommender systems are widely applied in digital platforms such as news websites to personalize services based on user preferences. In most of users anonymous and the only available data is sequences items sessions. Due this, typical collaborative filtering methods, which highly many applications, not effective recommendations. this context, session-based recommenders able recommend next given...

<p>There is an enormous number of mobile apps, leading users to be concerned about the security and privacy their data. But few are aware what meant by app permissions, which sometimes do not illustrate kind data gathered. Therefore, still risks privacy, with little knowledge experience awareness. Users depend on ratings, may fake, or keep track sense install app, like read reviews. To so...

Most context-aware recommender systems in the literature that use context modelling have the tendency to develop domain and application specific context models that limit, even eliminate any reuse and sharing capabilities. Developers and researchers in the field struggle to design their own context models without having a good understanding of context and without using any reference models for ...