Multimodal Fusion And Sparse Attention-based Alignment Model for Long Sequential Recommendation

TL;DR

MUFASA is a novel multimodal recommendation model that effectively fuses item content and models long-term user interests using sparse attention, leading to improved recommendation accuracy and real-world performance.

Contribution

The paper introduces MUFASA, combining a multimodal fusion layer with a sparse attention-based alignment layer for long sequential recommendation, addressing content understanding and interest modeling challenges.

Findings

MUFASA outperforms state-of-the-art baselines on benchmark datasets.

Online A/B tests show significant improvements in production environments.

The model effectively captures multi-grained user interests across long sequences.

Abstract

Recent advances in multimodal recommendation enable richer item understanding, while modeling users' multi-scale interests across temporal horizons has attracted growing attention. However, effectively exploiting multimodal item sequences and mining multi-grained user interests to substantially bridge the gap between content comprehension and recommendation remain challenging. To address these issues, we propose MUFASA, a MUltimodal Fusion And Sparse Attention-based Alignment model for long sequential recommendation. Our model comprises two core components. First, the Multimodal Fusion Layer (MFL) leverages item titles as a cross-genre semantic anchor and is trained with a joint objective of four tailored losses that promote: (i) cross-genre semantic alignment, (ii) alignment to the collaborative space for recommendation, (iii) preserving the similarity structure defined by titles and preventing modality representation collapse, and (iv) distributional regularization of the fusion space. This yields high-quality fused item representations for further preference alignment. Second, the Sparse Attention-guided Alignment Layer (SAL) scales to long user-behavior sequences via a multi-granularity sparse attention mechanism, which incorporates windowed attention, block-level attention, and selective attention, to capture user interests hierarchically and across temporal horizons. SAL explicitly models both the evolution of coherent interest blocks and fine-grained intra-block variations, producing robust user and item representations. Extensive experiments on real-world benchmarks show that MUFASA consistently surpasses state-of-the-art baselines. Moreover, online A/B tests demonstrate significant gains in production, confirming MUFASA's effectiveness in leveraging multimodal cues and accurately capturing diverse user preferences.