基于时尚图谱增强的个性化互补服装推荐

Recent years, people have a higher aesthetic pursuit of fashion clothing matching. Therefore, personalized com-plementary clothing recommendation, that is, to recommend complementary clothes to users for matching their existing garments, has attracted increasing research attention. Different from the general recommendation tasks (e.g., movie rec-ommendation), the recommended items in the context of personalized complementary clothing recommendation must meet two requirements: 1) match with the given item and 2) satisfy with the user's preference. Accordingly, existing re-search efforts mainly focus on modeling the item-item compatibility interaction and the user-item preference interaction based on the multi-modal data of fashion items to recommend items. One deficiency is that they regard each item-item or user-item interaction as an independent data instance, while overlooking the attribute information of items and the high-order relations among fashion entities, i.e., users, items, and attributes. In fact, items (e.g., bottoms) that go well with one item (e.g., a top) are more likely to share certain underlying attribute patterns (e.g., color), while users with similar tastes tend to choose items with similar attributes. Obviously, these high-order relations among fashion entities convey much implicit collaborative signals towards the item compatibility modeling and user preference modeling, and are critical for personalized complementary clothing recommendation. In light of this, in this work, we build a large-scale collabora-tive fashion graph to investigate the utility of high-order relations among fashion entities based on the Graph Convolutional Neural Networks(GNNs), in the context of personalized complementary clothing recommendation. In particular, we propose a new fashion graph-enhanced personalized complementary clothing recommendation model, dubbed as FG-PCCR, which consists of two key components: the independent one-order interaction modeling and the collaborative high-order interaction modeling. On the one hand, the independent one-order interaction modeling module, based on visual and textual modal data, is dedicated to the comprehensive modeling of item-item matching interaction and user-item preference interaction respectively by using neural network and matrix decomposition methods. On the other hand, the collaborative high-order interaction module is based on the constructed collaborative fashion graph, and distills the higher-order collaborative signals through the Graph Neural Networks using the information transmission mechanism to further enrich the vector representation of users and items. The FG-PCCR can effectively integrate the complex high-order relational information between fashion entities, the representation learning of users and items, and then improve the modeling effect of personalized clothing compatibility. Finally, for a given user and target top, the personalized compatibility score of the matching undergarment is obtained. In addition, extensive experiments on the real-world dataset have demonstrated the superiority of the proposed scheme over the state-of-the-art methods.