A Novel Group Recommendation Mechanism From the Perspective of Preference Distribution

As people have become more and more connected, there are certain scenarios where items need to be recommended to groups of users rather than individual user, which motivate studies on group recommender systems (GRSs). However, developing GRSs is not an easy task, because a group consists of multiple members with heterogeneous preferences. How to make a trade-off among their preferences remains challenging. Existing works almost aggregate members' preferences into forms of single values as group profile. However, simple aggregations fail to well reflect comprehensive group profile when it comes to groups with highly conflicting preferences. In this paper, we propose Greption, a novel group recommendation mechanism from the perspective of preference distribution. First, based on preference distributions toward items in training set, a multi-dimensional support vector regression model is established to predict preference distributions toward candidate items. Then, through a modified VIKOR method, we transform the process of selecting items for a group into a multi-criteria decision making process. Furthermore, the Greption is extended to be able to handle data sparsity. Specifically, we propose two heuristic schemes for this purpose. And we present a set of experiments to evaluate the efficiency of the Greption.