MISSRec: Pre-training and Transferring Multi-modal Interest-aware
Sequence Representation for Recommendation
- URL: http://arxiv.org/abs/2308.11175v2
- Date: Mon, 23 Oct 2023 10:46:07 GMT
- Title: MISSRec: Pre-training and Transferring Multi-modal Interest-aware
Sequence Representation for Recommendation
- Authors: Jinpeng Wang, Ziyun Zeng, Yunxiao Wang, Yuting Wang, Xingyu Lu,
Tianxiang Li, Jun Yuan, Rui Zhang, Hai-Tao Zheng, Shu-Tao Xia
- Abstract summary: We propose MISSRec, a multi-modal pre-training and transfer learning framework for sequential recommendation.
On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests.
On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation.
- Score: 61.45986275328629
- License: http://creativecommons.org/licenses/by/4.0/
- 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 user interests 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 a practical solution for real-world recommendation scenarios. Data
and code are available on \url{https://github.com/gimpong/MM23-MISSRec}.
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