A New Approach for Fairness Increment of Consensus-driven Group Recommender Systems Based on Choquet Integral

It has been witnessed in recent years for the rising of Group recommender systems (GRSs) in most e-commerce and tourism applications like Booking.com, Traveloka.com, Amazon, etc. One of the most concerned problems in GRSs is to guarantee the fairness between users in a group so-called the consensus-driven group recommender system. This paper proposes a new flexible alternative that embeds a fuzzy measure to aggregation operators of consensus process to improve fairness of group recommendation and deals with group member interaction. Choquet integral is used to build a fuzzy measure based on group member interactions and to seek a better fairness recommendation. The empirical results on the benchmark datasets show the incremental advances of the proposal for dealing with group member interactions and the issue of fairness in Consensus-driven GRS.

Context-Aware Music Recommender Systems for Groups: A Comparative Study

Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group music consumption has proliferated in this domain, not just physically, as in the past, but virtually in rooms or messaging groups created for specific purposes, such as studying, training, or meeting friends. Single-user recommender systems are no longer valid in this situation, and group recommender systems are needed to recommend music to groups of users, taking into account their individual preferences and the context of the group (when listening to music). In this paper, a group recommender system in the music domain is proposed, and an extensive comparative study is conducted, involving different collaborative filtering algorithms and aggregation methods.

Integrating Collaboration and Leadership in Conversational Group Recommender Systems

Recent observational studies highlight the importance of considering the interactions between users in the group recommendation process, but to date their integration has been marginal. In this article, we propose a collaborative model based on the social interactions that take place in a web-based conversational group recommender system. The collaborative model allows the group recommender to implicitly infer the different roles within the group, namely, collaborative and leader user(s). Moreover, it serves as the basis of several novel collaboration-based consensus strategies that integrate both individual and social interactions in the group recommendation process. A live-user evaluation confirms that our approach accurately identifies the collaborative and leader users in a group and produces more effective recommendations.

Dynamic Group Recommendation Algorithm Based on Member Activity Level

The rapid development of social networks has led to an increased desire for group entertainment consumption, making the study of group recommender systems a hotspot. Existing group recommender systems focus too much on member preferences and ignore the impact of member activity level on recommendation results. To this end, a dynamic group recommendation algorithm based on the activity level of members is proposed. Firstly, the algorithm predicts the unknown preferences of members using a time-series-oriented rating prediction model. Secondly, considering the dynamic change of member activity level, the group profile is generated by designing a sliding time window to investigate the recent activity level of each member in the group at the recommended moment, and preference is aggregated based on the recent activity level of members. Finally, the group recommendations are generated based on the group profile. The experimental results show that the algorithm in this paper achieves a better recommendation result.