Latent Structures Mining with Contrastive Modality Fusion for Multimedia Recommendation

• URL: http://arxiv.org/abs/2111.00678v1
• Date: Mon, 1 Nov 2021 03:37:02 GMT
• Title: Latent Structures Mining with Contrastive Modality Fusion for Multimedia Recommendation
• Authors: Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu, Liang Wang
• Abstract summary: We argue that the latent semantic item-item structures underlying multimodal contents could be beneficial for learning better item representations. We devise a novel modality-aware structure learning module, which learns item-item relationships for each modality.
• Score: 22.701371886522494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years have witnessed growing interests in multimedia recommendation, which aims to predict whether a user will interact with an item with multimodal contents. Previous studies focus on modeling user-item interactions with multimodal features included as side information. However, this scheme is not well-designed for multimedia recommendation. Firstly, only collaborative item-item relationships are implicitly modeled through high-order item-user-item co-occurrences. We argue that the latent semantic item-item structures underlying these multimodal contents could be beneficial for learning better item representations and assist the recommender models to comprehensively discover candidate items. Secondly, previous studies disregard the fine-grained multimodal fusion. Although having access to multiple modalities might allow us to capture rich information, we argue that the simple coarse-grained fusion by linear combination or concatenation in previous work is insufficient to fully understand content information and item relationships.To this end, we propose a latent structure MIning with ContRastive mOdality fusion method (MICRO for brevity). To be specific, we devise a novel modality-aware structure learning module, which learns item-item relationships for each modality. Based on the learned modality-aware latent item relationships, we perform graph convolutions that explicitly inject item affinities to modality-aware item representations. Then, we design a novel contrastive method to fuse multimodal features. These enriched item representations can be plugged into existing collaborative filtering methods to make more accurate recommendations. Extensive experiments on real-world datasets demonstrate the superiority of our method over state-of-the-art baselines.

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