Interact and Decide: Medley of Sub Attention Networks for Effective Group Recommendation
This paper proposes Medley of Sub Attention Networks (MoSAN) a new novel neural architecture for the group recommendation task. Group level recommendation is known to be a challenging task, in which intricate group dynamics have to be considered. As such, this is to be contrasted with the standard recommendation problem where recommendations are personalized with respect to a single user. Our proposed approach hinges upon the key intuition that the decision making process (in groups) is generally dynamic, i.e. a user's decision is highly dependent on the other group members. All in all, our key motivation manifests in a form of an attentive neural model that captures fine grained interactions between group members. In our MoSAN model, each sub attention module is representative of a single member, which models a user's preference with respect to all other group members. Subsequently, a Medley of Sub Attention modules is then used to collectively make the group's final decision. Overall, our proposed model is both expressive and effective. Via a series of extensive experiments, we show that MoSAN not only achieves state of the art performance but also improves standard baselines by a considerable margin.
SIGIR