From Group Recommendations to Group Formation

There has been significant recent interest in the area of group recommendations, where, given groups of users of a recommender system, one wants to recommend top-k items to a group that maximize the satisfaction of the group members, according to a chosen semantics of group satisfaction. Examples semantics of satisfaction of a recommended itemset to a group include the so-called least misery (LM) and aggregate voting (AV). We consider the complementary problem of how to form groups such that the users in the formed groups are most satisfied with the suggested top-k recommendations. We assume that the recommendations will be generated according to one of the two group recommendation semantics - LM or AV. Rather than assuming groups are given, or rely on ad hoc group formation dynamics, our framework allows a strategic approach for forming groups of users in order to maximize satisfaction. We show that the problem is NP-hard to solve optimally under both semantics. Furthermore, we develop two efficient algorithms for group formation under LM and show that they achieve bounded absolute error. We develop efficient heuristic algorithms for group formation under AV. We validate our results and demonstrate the scalability and effectiveness of our group formation algorithms on two large real data sets.Comment: 14 pages, 22 figure

Group Recommendation with Temporal Affinities

International audienceWe examine the problem of recommending items to ad-hoc user groups. Group recommendation in collaborative rating datasets has received increased attention recently and has raised novel challenges. Different consensus functions that aggregate the ratings of group members with varying semantics ranging from least misery to pairwise disagreement, have been studied. In this paper, we explore a new dimension when computing group recommendations, that is, affinity between group members and its evolution over time. We extend existing group recommendation semantics to include temporal affinity in recommendations and design GRECA, an efficient algorithm that produces temporal affinity-aware recommendations for ad-hoc groups. We run extensive experiments that show substantial improvements in group recommendation quality when accounting for affinity while maintaining very good performance

Diversifying Group Recommendation

Recommender-systems has been a significant research direction in both literature and practice. The core of recommender systems are the recommendation mechanisms, which suggest to a user a selected set of items supposed to match user true intent, based on existing user preferences. In some scenarios, the items to be recommended are not intended for personal use but a group of users. Group recommendation is rather more since group members have wide-ranging levels of interests and often involve conflicts. However, group recommendation endures the over-specification problem, in which the presumingly relevant items do not necessarily match true user intent. In this paper, we address the problem of diversity in group recommendation by improving the chance of returning at least one piece of information that embraces group satisfaction. We proposed a bounded algorithm that finds a subset of items with maximal group utility and maximal variety of information. Experiments on real-world rating datasets show the efficiency and effectiveness of our approach

Exploiting group recommendation functions for flexible preferences

International audienc

Exploiting group recommendation functions for flexible preferences

International audienc