MTSTRec: Multimodal Time-Aligned Shared Token Recommender

Yen-Jung Hsu, Ming-Yi Hong, Che Lin

Keywords: multimodal sequential recommendation, time-aligned shared token, image style representation, large language model

Abstract: Sequential recommendation in e-commerce leverages users' anonymous browsing histories to offer personalized product suggestions without relying on personal information. While item ID-based sequential recommendations are commonly used, they often fail to fully capture the diverse factors influencing user preferences, such as textual descriptions, visual content, and pricing. These factors represent distinct modalities in recommender systems. Existing multimodal sequential recommendation models typically employ either early or late fusion of different modalities, overlooking the alignment of corresponding positions in time of product sequences that represent users' browsing preferences. To address these limitations, this paper proposes a unified framework for multimodal fusion in recommender systems, introducing the Multimodal Time-aligned Shared Token Recommender (MTSTRec). MTSTRec leverages a transformer-based architecture that incorporates a single time-aligned shared token for each product, allowing for efficient cross-modality fusion that also aligns in time. This approach not only preserves the distinct contributions of each modality but also aligns them to better capture user preferences. Additionally, the model extracts rich features from text, images, and other product data, offering a more comprehensive representation of user decision-making in e-commerce. Extensive experiments demonstrate that MTSTRec achieves state-of-the-art performance across multiple sequential recommendation benchmarks, significantly improving upon existing multimodal fusion strategies.

Primary Area: learning on time series and dynamical systems

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Submission Number: 9731