Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective recommender system models because they model rich relational information. The ever-growing volume can make training GNNs prohibitively expensive. To address this, previous attempts propose to train GNN incrementally new...

Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years. This has led more research being done this area stimulated by omnipresence of smartphones and latest web technologies. These are able detect specific user needs adapt recommendations actual context. In research, we present a comprehensive review context-aware developed for ...

Context aware recommender systems (CARS) adapt to the specific situation in which the recommended item will be consumed. So, for instance, music recommendations while the user is traveling by car should take into account the current traffic condition or the driver’s mood. This requires the acquisition of ratings for items in several alternative contextual situations, to extract from this data t...

In this paper we revise the state of the art on personality-aware recommender systems, identifying main research trends and achievements up to date, and discussing open issues that may be addressed in the future.

The design of recommendation algorithms aware the user’s context has been subject great interest in scientific community, especially music domain where contextual factors have a significant impact on recommendations. In this type system, information can come from different sources such as specific time day, physical activity, and geolocation, among many others. This is generally obtained by ele...

Recommender systems are intelligent systems which make suggestions about user items. Recommender system has become an important part of any entertainment or marketing website. As the recommender system has become so important it is a hot topic for any researcher. First paper on Recommender System was published in year 1998 since then more than 300 papers have been published in many of different...

The aim of this study was to investigate the use and potential of the psychological theory of human-behavior modeling, called the Theory of Planned Behavior (TPB), in a user-modeling domain. We performed a user experiment involving a well-studied problem of user modeling, i.e., a recommender system (RS) for movies. As a part of the TPB, a survey to estimate the behavioral, normative and control...

A significant remaining challenge for existing recommender systems is that users may not trust either inaccurate recommendation or lack of explanation. Thus, it becomes critical to embrace a trustworthy system. This survey provides systematic summary three categories issues in systems: social-aware systems, which leverage users’ social relationships; robust filter untruthful information, noises...