盒子里的思考：学习超立方体表示用于组推荐
Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
摘要：
群组推荐是对传统个性化推荐的一种超越，它是向一组用户推荐满意的推荐项目。在群体推荐中，核心是设计偏好聚合函数，以获得群体成员偏好的高质量汇总。这样的用户和组偏好通常被表示为向量空间中的点（即，嵌入），其中多个用户嵌入被压缩成一个，以便于对组项对进行排名。然而，所得到的组表示，作为点，缺乏足够的灵活性和能力来考虑多方面的用户偏好。此外，基于点嵌入的偏好聚合是一个群体的决策过程，其中所有用户必须同意在每个嵌入维的某个值，而不是一个可协商的间隔不太忠实的反映。在本文中，我们提出了一种新的表示群通过概念的超立方体，这是子空间包含无数的点在向量空间。具体来说，我们设计的超立方体推荐（CubeRec）自适应学习组超立方体从用户嵌入在偏好聚合过程中的信息损失最小，并利用一个改进的距离度量来衡量组超立方体和项目点之间的亲和力。此外，针对群组推荐中数据稀疏性的长期问题，我们充分利用超立方体的几何表达能力，并创新性地通过交叉两个群组来引入自我监督。在四个真实世界数据集上的实验验证了CubeRec相对于最先进基线的优越性。
英文摘要：
As a step beyond traditional personalized recommendation, group recommendation is the task of suggesting items that can satisfy a group of users. In group recommendation, the core is to design preference aggregation functions to obtain a quality summary of all group members' preferences. Such user and group preferences are commonly represented as points in the vector space (i.e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs. However, the resulted group representations, as points, lack adequate flexibility and capacity to account for the multi-faceted user preferences. Also, the point embedding-based preference aggregation is a less faithful reflection of a group's decision-making process, where all users have to agree on a certain value in each embedding dimension instead of a negotiable interval. In this paper, we propose a novel representation of groups via the notion of hypercubes, which are subspaces containing innumerable points in the vector space. Specifically, we design the hypercube recommender (CubeRec) to adaptively learn group hypercubes from user embeddings with minimal information loss during preference aggregation, and to leverage a revamped distance metric to measure the affinity between group hypercubes and item points. Moreover, to counteract the long-standing issue of data sparsity in group recommendation, we make full use of the geometric expressiveness of hypercubes and innovatively incorporate self-supervision by intersecting two groups. Experiments on four real-world datasets have validated the superiority of CubeRec over state-of-the-art baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
