Multimodal Enhancement of Sequential Recommendation
This work addresses recommendation systems for e-commerce users by integrating multimodal and sequential data, though it appears incremental as it builds on existing transformer-based approaches.
The authors tackled the problem of sequential recommendation by proposing MuSTRec, a framework that unifies multimodal and sequential recommendation paradigms, achieving up to 33.5% improvement over state-of-the-art baselines on Amazon datasets.
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.