Attention-based Group Recommendation

Group recommendation aims to recommend items for a group of users, e.g., recommending a restaurant for a group of colleagues. The group recommendation problem is challenging, in that a good model should understand the group decision making process appropriately: users are likely to follow decisions of only a few users, who are group's leaders or experts. To address this challenge, we propose using an attention mechanism to capture the impact of each user in a group. Specifically, our model learns the influence weight of each user in a group and recommends items to the group based on its members' weighted preferences. Moreover, our model can dynamically adjust the weight of each user across the groups; thus, the model provides a new and flexible method to model the complicated group decision making process, which differentiates us from other existing solutions. Through extensive experiments, it has demonstrated that our model significantly outperforms baseline methods for the group recommendation problem.