Leveraging Two Types of Global Graph for Sequential Fashion Recommendation

TL;DR

This paper introduces a novel sequential fashion recommendation model that leverages two global graphs—user-item interaction and item-item transition graphs—using LightGCN to improve user and item representations, validated by extensive experiments.

Contribution

The paper proposes a new approach that incorporates two types of global graphs with LightGCN for enhanced sequential fashion recommendation, addressing data sparsity and efficiency issues.

Findings

Effective in capturing user preferences and item transitions

Outperforms baseline models on established datasets

Improves recommendation accuracy and efficiency

Abstract

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 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.