Multimodal Enhancement of Sequential Recommendation

• URL: http://arxiv.org/abs/2602.07207v1
• Date: Fri, 06 Feb 2026 21:32:56 GMT
• Title: Multimodal Enhancement of Sequential Recommendation
• Authors: Bucher Sahyouni, Matthew Vowels, Liqun Chen, Simon Hadfield,
• Abstract summary: We propose a novel recommender framework, MuSTRec, that unifies multimodal and sequential recommendation paradigms.<n>MuSTRec captures cross-item similarities and collaborative filtering signals, by building item-item graphs from extracted text and visual features.<n>Across multiple Amazon datasets, MuSTRec demonstrates superior performance (up to 33.5% improvement) over multimodal and sequential state-of-the-art baselines.
• Score: 10.466765832314683
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a novel recommender framework, MuSTRec (Multimodal and Sequential Transformer-based Recommendation), that unifies multimodal and sequential recommendation paradigms. MuSTRec captures cross-item similarities and collaborative filtering signals, by building item-item graphs from extracted text and visual features. A frequency-based self-attention module additionally captures the short- and long-term user preferences. Across multiple Amazon datasets, MuSTRec demonstrates superior performance (up to 33.5% improvement) over multimodal and sequential state-of-the-art baselines. Finally, we detail some interesting facets of this new recommendation paradigm. These include the need for a new data partitioning regime, and a demonstration of how integrating user embeddings into sequential recommendation leads to drastically increased short-term metrics (up to 200% improvement) on smaller datasets. Our code is availabe at https://anonymous.4open.science/r/MuSTRec-D32B/ and will be made publicly available.

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