Personalized recommender systems aim to predict users' preferences for items. It has become an indispensable part of online services. Online social platforms enable users to form groups based on their common interests. The users' group participation on social platforms reveals their interests and can be utilized as side information to mitigate the data sparsity and cold-start problem in recommender systems. Users join different groups out of different interests. In this paper, we generate group representation from the user's interests and propose IGRec (Interest-based Group enhanced Recommendation) to utilize the group information accurately. It consists of four modules. (1) Interest disentangler via self-gating that disentangles users' interests from their initial embedding representation. (2) Interest aggregator that generates the interest-based group representation by Gumbel-Softmax aggregation on the group members' interests. (3) Interest-based group aggregation that fuses user's representation with the participated group representation. (4) A dual-trained rating prediction module to utilize both user-item and group-item interactions. We conduct extensive experiments on three publicly available datasets. Results show IGRec can effectively alleviate the data sparsity problem and enhance the recommender system with interest-based group representation. Experiments on the group recommendation task further show the informativeness of interest-based group representation.

翻译：个性化推荐系统旨在预测用户对物品的偏好，已成为在线服务中不可或缺的组成部分。在线社交平台允许用户基于共同兴趣组建群体。用户在社交平台上的群体参与行为揭示了其兴趣偏好，可作为辅助信息缓解推荐系统中的数据稀疏性与冷启动问题。用户因不同兴趣加入不同群体。本文从用户兴趣出发生成群体表征，并提出IGRec（基于兴趣的群体增强推荐）以精准利用群体信息。该方法包含四个模块：（1）通过自门控机制从用户初始嵌入表征中解耦用户兴趣的兴趣解耦器；（2）通过Gumbel-Softmax聚合群体成员兴趣生成基于兴趣的群体表征的兴趣聚合器；（3）将用户表征与所参与群体表征融合的基于兴趣的群体聚合模块；（4）利用用户-物品与群体-物品双重交互的双训练评分预测模块。我们在三个公开数据集上进行大量实验，结果表明IGRec能有效缓解数据稀疏性问题，并通过基于兴趣的群体表征增强推荐系统。群体推荐任务的进一步实验验证了基于兴趣的群体表征的信息丰富性。