Cross-domain recommendation (CDR) is an effective way to alleviate the data sparsity problem. Content-based CDR is one of the most promising branches since most kinds of products can be described by a piece of text, especially when cold-start users or items have few interactions. However, two vital issues are still under-explored: (1) From the content modeling perspective, sufficient long-text descriptions are usually scarce in a real recommender system, more often the light-weight textual features, such as a few keywords or tags, are more accessible, which is improperly modeled by existing methods. (2) From the CDR perspective, not all inter-domain interests are helpful to infer intra-domain interests. Caused by domain-specific features, there are part of signals benefiting for recommendation in the source domain but harmful for that in the target domain. Therefore, how to distill useful interests is crucial. To tackle the above two problems, we propose a metapath and multi-interest aggregated graph neural network (M2GNN). Specifically, to model the tag-based contents, we construct a heterogeneous information network to hold the semantic relatedness between users, items, and tags in all domains. The metapath schema is predefined according to domain-specific knowledge, with one metapath for one domain. User representations are learned by GNN with a hierarchical aggregation framework, where the intra-metapath aggregation firstly filters out trivial tags and the inter-metapath aggregation further filters out useless interests. Offline experiments and online A/B tests demonstrate that M2GNN achieves significant improvements over the state-of-the-art methods and current industrial recommender system in Dianping, respectively. Further analysis shows that M2GNN offers an interpretable recommendation.

翻译：跨域推荐（CDR）是缓解数据稀疏问题的有效方法。基于内容的CDR是最具前景的分支之一，因为大多数商品可通过文本描述，尤其当冷启动用户或物品缺少交互时。然而，两个关键问题尚未充分研究：（1）从内容建模角度，实际推荐系统中通常缺乏充分的长文本描述，而轻量级文本特征（如少量关键词或标签）更易获取，但现有方法对此建模不足。（2）从跨域推荐角度，并非所有跨域兴趣都有助于推断域内兴趣。由于域特定特征的存在，部分信号在源域中有利于推荐，却在目标域中起反作用。因此，如何提取有效兴趣至关重要。针对上述问题，本文提出元路径与多兴趣聚合图神经网络（M2GNN）。具体而言，为建模基于标签的内容，我们构建异构信息网络以保留所有域中用户、物品和标签间的语义关联。根据域特定知识预定义元路径模式，每个域对应一条元路径。用户表示通过图神经网络分层聚合框架学习：元路径内聚合首先过滤无关标签，元路径间聚合进一步滤除无用兴趣。离线实验和在线A/B测试表明，M2GNN在各自对比中显著优于现有最先进方法及大众点评当前工业推荐系统。进一步分析显示，M2GNN具有可解释推荐能力。