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CROSS: Feedback-Oriented Multi-Modal Dynamic Alignment in Recommendation Systems
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AI
DOI：10.1145/3734527.png)
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Abstract
En 中文
Aligning the multi-modal content and ID embeddings is crucial in multi-modal recommendation systems. Existing solutions typically adopt a bidirectional alignment paradigm. Our prior work, FETTLE, challenges this paradigm by proposing a one-way directional alignment at the item level, thus reducing the negative impact of low-quality modalities. However, FETTLE leaves two open questions: (1) when is one-way directional alignment optimal, and (2) how to incorporate collaborative signals to enhance alignment? We present CROSS (feedbaCk-oRiented multi-mOdal alignment in recommendation SyStem), a plug-and-play framework that extends FETTLE by introducing three major advancements. First, we introduce Dynamic Item-Level Alignment, which dynamically calibrates the strength of each modality via a variance-based compensation mechanism, mitigating the risk of overshadowing weaker modalities in the early stages of training. Second, we develop Multi-grained Collaborative Alignment, which introduces a medium-granularity alignment strategy based on neighboring items that share similar user feedback profiles. This neighbor-level alignment effectively balances noisy user interactions and excessive smoothing across items. Third, we conduct extensive experiments on more real-world datasets and show that CROSS significantly boosts the performance of both collaborative filtering (CF) models and multi-modal recommendation (MRS) approaches, achieving 21.52%-70.78% average improvement on CF backbones and 8.70%-20.73% on MRS backbones. Compared with FETTLE, CROSS achieves additional improvements of 3.82%-5.24%.
Keywords:
Recommender systems
multi-modal recommendation
multi-modality alignment
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