Personalized Fashion Compatibility Modeling via Metapath-guided Heterogeneous Graph Learning
- Publisher:
- ASSOC COMPUTING MACHINERY
- Publication Type:
- Conference Proceeding
- Citation:
- SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022, pp. 482-491
- Issue Date:
- 2022-07-06
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| sigir2022-camera-ready.pdf | Published version | 1.44 MB |
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Fashion Compatibility Modeling (FCM) is a new yet challenging task, which aims to automatically access the matching degree among a set of complementary items. Most of existing methods evaluate the fashion compatibility from the common perspective, but overlook the user's personal preference. Inspired by this, a few pioneers study the Personalized Fashion Compatibility Modeling (PFCM). Despite their significance, these PFCM methods mainly concentrate on the user and item entities, as well as their interactions, but ignore the attribute entities, which contain rich semantics. To address this problem, we propose to fully explore the related entities and their relations involved in PFCM to boost the PFCM performance. This is, however, non-trivial due to the heterogeneous contents of different entities, embeddings for new users, and various high-order relations. Towards these ends, we present a novel metapath-guided personalized fashion compatibility modeling, dubbed as MG-PFCM. In particular, we creatively build a heterogeneous graph to unify the three types of entities (i.e., users, items, and attributes) and their relations (i.e., user-item interactions, item-item matching relations, and item-attribute association relations). Thereafter, we design a multi-modal content-oriented user embedding module to learn user representations by inheriting the contents of their interacted items. Meanwhile, we define the user-oriented and item-oriented metapaths, and perform the metapath-guided heterogeneous graph learning to enhance the user and item embeddings. In addition, we introduce the contrastive regularization to improve the model performance. We conduct extensive experiments on the real-world benchmark dataset, which verifies the superiority of our proposed scheme over several cutting-edge baselines. As a byproduct, we have released our source codes to benefit other researchers.
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