Semantic Item Graph Enhancement for Multimodal Recommendation
Xiaoxiong Zhang, Xin Zhou, Zhiwei Zeng, Dusit Niyato, Zhiqi Shen

TL;DR
This paper introduces a novel multimodal recommendation framework that enhances semantic item graphs by infusing collaborative signals, employing contrastive learning with noise-robust embeddings, and aligning multiple semantic representations for improved recommendation accuracy.
Contribution
The paper proposes a new method to improve multimodal recommendation by infusing collaborative signals into semantic graphs, using contrastive learning with personalized perturbations, and aligning multiple semantic representations.
Findings
Significant performance improvements on four benchmark datasets.
Effective noise reduction in semantic graphs through contrastive learning.
Enhanced representation consistency via dual alignment mechanisms.
Abstract
Multimodal recommendation systems have attracted increasing attention for their improved performance by leveraging items' multimodal information. Prior methods often build modality-specific item-item semantic graphs from raw modality features and use them as supplementary structures alongside the user-item interaction graph to enhance user preference learning. However, these semantic graphs suffer from semantic deficiencies, including (1) insufficient modeling of collaborative signals among items and (2) structural distortions introduced by noise in raw modality features, ultimately compromising performance. To address these issues, we first extract collaborative signals from the interaction graph and infuse them into each modality-specific item semantic graph to enhance semantic modeling. Then, we design a modulus-based personalized embedding perturbation mechanism that injects…
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Taxonomy
TopicsRecommender Systems and Techniques · Sentiment Analysis and Opinion Mining · Topic Modeling
