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• W4386114032 abstract "The goal of sequential recommendation (SR) is to predict a user's potential interested items based on her/his historical interaction sequences. Most existing sequential recommenders are developed based on ID features, which, despite their widespread use, often underperform with sparse IDs and struggle with the cold-start problem. Besides, inconsistent ID mappings hinder the model's transferability, isolating similar recommendation domains that could have been co-optimized. This paper aims to address these issues by exploring the potential of multi-modal information in learning robust and generalizable sequence representations. We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal synergy while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation. On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation, providing more precise matching between users and items. We pre-train the model with contrastive learning objectives and fine-tune it in an efficient manner. Extensive experiments demonstrate the effectiveness and flexibility of MISSRec, promising an practical solution for real-world recommendation scenarios." @default.
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• W4386114032 date "2023-08-22" @default.
• W4386114032 modified "2023-10-16" @default.
• W4386114032 title "MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation" @default.
• W4386114032 doi "https://doi.org/10.1145/3581783.3611967" @default.
• W4386114032 hasPublicationYear "2023" @default.
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