2021/05/30 by Yujuan Ding, Yunshan Ma, Wai Keung Wong +1 · 23 citations
Computer Science · #Collaborative filtering #Generative Adversarial Networks and Image Synthesis #Graph #Image Retrieval and Classification Techniques #Key (lock) #Leverage (statistics) #Recommender Systems and Techniques #Recommender system #Transition (genetics) #cs.IR #cs.MM
paper · pdf · doi:10.1145/3460426.3463638
openalex created_date 2021/05/24 · arxiv created 2021/05/30 · arxiv updated 2021/06/01 · openalex publication_date 2021/08/24 · openalex updated_date 2026/08/05
Sequential fashion recommendation is of great significance in online fashion shopping, which accounts for an increasing portion of either fashion retailing or online e-commerce. The key to building an effective sequential fashion recommendation model lies in capturing two types of patterns: the personal fashion preference of users and the transitional relationships between adjacent items. The two types of patterns are usually related to user-item interaction and item-item transition modeling respectively. However, due to the large sets of users and items as well as the sparse historical interactions, it is difficult to train an effective and efficient sequential fashion recommendation model. To tackle these problems, we propose to leverage two types of global graph, i.e., the user-item interaction graph and item-item transition graph, to obtain enhanced user and item representations by incorporating higher-order connections over the graphs. In addition, we adopt the graph kernel of LightGCN [9] for the information propagation in both graphs and propose a new design for item-item transition graph. Extensive experiments on two established sequential fashion recommendation datasets validate the effectiveness and efficiency of our approach.