SOIL: Contrastive Second-Order Interest Learning for Multimodal Recommendation
Abstract: Mainstream multimodal recommender systems are designed to learn user interest by analyzing user-item interaction graphs. However, what they learn about user interest needs to be completed because historical interactions only record items that best match user interest (i.e., the first-order interest), while suboptimal items are absent. To fully exploit user interest, we propose a Second-Order Interest Learning (SOIL) framework to retrieve second-order interest from unrecorded suboptimal items. In this framework, we build a user-item interaction graph augmented by second-order interest, an interest-aware item-item graph for the visual modality, and a similar graph for the textual modality. In our work, all three graphs are constructed from user-item interaction records and multimodal feature similarity. Similarly to other graph-based approaches, we apply graph convolutional networks to each of the three graphs to learn representations of users and items. To improve the exploitation of both first-order and second-order interest, we optimize the model by implementing contrastive learning modules for user and item representations at both the user-item and item-item levels. The proposed framework is evaluated on three real-world public datasets in online shopping scenarios. Experimental results verify that our method is able to significantly improve prediction performance. For instance, our method outperforms the previous state-of-the-art method MGCN by an average of $8.1\%$ in terms of Recall@10.
Primary Subject Area: [Engagement] Multimedia Search and Recommendation
Secondary Subject Area: [Experience] Multimedia Applications
Relevance To Conference: This work handles multimodal recommendation systems, incorporating both visual and textual modalities. A novel approach is introduced, leveraging multimodal feature information and user interaction records to mine the second-order interest of users. Extensive experiments have verified that this method significantly improves the performance of multimodal recommendations, contributing to the multimodal community by refining user experience and interaction.
Submission Number: 3012
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