Recommendations are important web services in the era of information explosion. Particularly, group recommendations aim to suggest new items to groups such that the members of groups are likely interested in. However, existing works still suffer from sparsity and cold-start issues (e.g., cold-start groups or items) for groups with few interactions on items. Most of them model the preferences or features of entities (i.e., users, items and groups) from heterogeneous interactions (i.e., user-item, group-item and user-group interactions) between two distinct types of entities, while ignoring the homogeneous interactions (i.e., user-user, item-item and group-group interactions) between entities of one type. To this end, we propose a new model, called H3Rec, which learns the representations of entities by developing two graph embedding layers based on an interaction graph of all entities. Specifically, the two graph embedding layers make full use of the hidden information in the Higher-order Heterogeneous and Homogeneous interactions of the graph. Therefore, H3Rec can alleviate the sparsity and cold-start issues and improve the performance of group recommendations. The experimental results on two real world datasets in different domains show the superiority of H3Rec in group recommendations, especially for cold-start groups and items.

中文翻译：

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H3Rec：Web 服务群组推荐的高阶异构和同构交互建模


推荐是信息爆炸时代重要的网络服务。特别是，群组推荐的目的是向群组推荐新的项目，以便群组成员可能感兴趣。然而，对于互动很少的群组来说，现有的工作仍然存在稀疏性和冷启动问题（例如，冷启动群组或项目）在物品上。它们中的大多数根据两种不同类型的实体之间的异构交互（即用户-项目、组-项目和用户-组交互）对实体（即用户、项目和组）的偏好或特征进行建模，而忽略同质交互一种类型的实体之间（即用户-用户、项目-项目和组-组交互）。为此，我们提出了一种名为 H3Rec 的新模型，它通过基于所有实体的交互图开发两个图嵌入层来学习实体的表示。具体来说，两个图嵌入层充分利用了图的高阶异质和同质交互中的隐藏信息。因此，H3Rec可以缓解稀疏性和冷启动问题，提高群组推荐的性能。在不同领域的两个现实世界数据集上的实验结果显示了 H3Rec 在组推荐方面的优越性，特别是对于冷启动组和项目。