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Bootstrap Latent Representations for Multi-modal Recommendation by Zhou, Xin; Zhou, Hongyu; Liu, Yong; Zeng, Zhiwei; Miao, Chunyan; Wang, Pengwei; You, Yuan; Jiang, Feijun is a scholarly article available to read on EtoBox.

What is Bootstrap Latent Representations for Multi-modal Recommendation about?

This paper studies the multi-modal recommendation problem, where the item multi-modality information (e.g., images and textual descriptions) is exploited to improve the recommendation accuracy. Besides the user-item interaction graph, existing state-of-the-art methods usually use auxiliary graphs (e.g., user-user or item-item relation graph) to augment the learned representations of users and/or items. These representations are often propagated and aggregated on auxiliary graphs using graph convolutional networks, which can be prohibitively expensive in computation and memory, especially for large graphs. Moreover, existing multi-modal recommendation methods usually leverage randomly sampled negative examples in Bayesian Personalized Ranking (BPR) loss to guide the learning of user/item representations, which increases the computational cost on large graphs and may also bring noisy supervision signals into the training process. To tackle the above issues, we propose a novel self-supervised multi-modal recommendation model, dubbed BM3, which requires neither augmentations from auxiliary graphs nor negative samples. Specifically, BM3 first bootstraps latent contrastive views from the

Author

Zhou, Xin; Zhou, Hongyu; Liu, Yong; Zeng, Zhiwei; Miao, Chunyan; Wang, Pengwei; You, Yuan; Jiang, Feijun

Published

2022

Language

EN