兼顾群体共识与个体偏好，提升群组推荐准确率
AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation
- 用超图神经网络捕捉群内群间关系，挖掘群体共识
- 通过自监督对齐任务，融合成员共同偏好与群体决策
- 在真实数据集上优于主流方法，适合社交推荐场景
群组活动是人类社会的重要行为，为群组提供个性化推荐称为群组推荐任务。现有方法通常分为两类：一是通过聚合成员偏好推断群体偏好，二是通过捕捉成员妥协后的共性决策推断群体共识。前者缺乏群组层面考量，后者忽略个体细粒度偏好。为此，本文提出新型群组推荐方法AlignGroup，同时关注群体共识与成员个体偏好以推断群组决策。具体而言，AlignGroup利用设计精良的超图神经网络高效学习群内与群间关系；创新性地引入自监督对齐任务，通过将群体共识与成员共同偏好对齐，捕捉细粒度群组决策。在两个真实数据集上的大量实验表明，AlignGroup在群组推荐和用户推荐任务上均超越当前最优方法，且多数基线效率更高。
原文摘要 · Abstract (English)
Group activities are important behaviors in human society, providing personalized recommendations for groups is referred to as the group recommendation task. Existing methods can usually be categorized into two strategies to infer group preferences: 1) determining group preferences by aggregating members' personalized preferences, and 2) inferring group consensus by capturing group members' coherent decisions after common compromises. However, the former would suffer from the lack of group-level considerations, and the latter overlooks the fine-grained preferences of individual users. To this end, we propose a novel group recommendation method AlignGroup, which focuses on both group consensus and individual preferences of group members to infer the group decision-making. Specifically, AlignGroup explores group consensus through a well-designed hypergraph neural network that efficiently learns intra- and inter-group relationships. Moreover, AlignGroup innovatively utilizes a self-supervised alignment task to capture fine-grained group decision-making by aligning the group consensus with members' common preferences. Extensive experiments on two real-world datasets validate that our AlignGroup outperforms the state-of-the-art on both the group recommendation task and the user recommendation task, as well as outperforms the efficiency of most baselines.
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