  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.
