@inproceedings{
su2024soil,
title={{SOIL}: Contrastive Second-Order Interest Learning for Multimodal Recommendation},
author={Hongzu Su and Jingjing Li and Fengling Li and Ke Lu and Lei Zhu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=NrHuNScX2p}
}
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.
SOIL: Contrastive Second-Order Interest Learning for Multimodal Recommendation
Hongzu Su Jingjing Li Fengling Li Ke Lu Lei Zhu