Please use this identifier to cite or link to this item: https://doi.org/10.1145/3397271.3401080
Title: Hierarchical Fashion Graph Network for Personalized Outfit Recommendation
Authors: Xingchen Li
Xiang Wang 
Xiangnan He
Long Chen
Jun Xiao
Tat-Seng Chua 
Keywords: Personalized Fashion Recommendation
Fashion Compatibility
Graph Neural Networks
Issue Date: 25-Jul-2020
Publisher: Association for Computing Machinery, Inc
Citation: Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, Tat-Seng Chua (2020-07-25). Hierarchical Fashion Graph Network for Personalized Outfit Recommendation. SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval : 159 - 168. ScholarBank@NUS Repository. https://doi.org/10.1145/3397271.3401080
Abstract: Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities.Distinct from other scenarios (e.g., social networking or content sharing) which recommend a single item (e.g., a friend or picture) to a user, outfit recommendation predicts user preference on a set of well-matched fashion items. Hence, performing high-quality personalized outfit recommendation should satisfy two requirements-1) the nice compatibility of fashion items and 2) the consistence with user preference. However, present works focus mainly on one of the requirements and only consider either user-outfit or outfit-item relationships, thereby easily leading to suboptimal representations and limiting the performance. In this work, we unify two tasks, fashion compatibility modeling and personalized outfit recommendation. Towards this end, we develop a new framework, Hierarchical Fashion Graph Network(HFGN), to model relationships among users, items, and outfits simultaneously. In particular, we construct a hierarchical structure upon user-outfit interactions and outfit-item mappings. We then get inspirations from recent graph neural networks, and employ the embedding propagation on such hierarchical graph, so as to aggregate item information into an outfit representation, and then refine a user's representation via his/her historical outfits. Furthermore, we jointly train these two tasks to optimize these representations. To demonstrate the effectiveness of HFGN, we conduct extensive experiments on a benchmark dataset, and HFGN achieves significant improvements over the state-of-the-art compatibility matching models like NGNN and outfit recommenders like FHN. © 2020 ACM.
Source Title: SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
URI: https://scholarbank.nus.edu.sg/handle/10635/178641
ISBN: 9781450000000
DOI: 10.1145/3397271.3401080
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