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Found 4 papers, 2 papers with code
Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation
Recent sequential recommendation models have combined pre-trained text embeddings of items with item ID embeddings to achieve superior recommendation performance.
Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning
To address this issue, we propose a novel pre-training framework, named Multimodal Sequence Mixup for Sequential Recommendation (MSM4SR), which leverages both users' sequential behaviors and items' multimodal content (\ie text and images) for effectively recommendation.
A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions
Recommendation systems have become popular and effective tools to help users discover their interesting items by modeling the user preference and item property based on implicit interactions (e. g., purchasing and clicking).
Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation
On top of the finding, we propose a model that enhances the dyadic relations by learning Dual RepresentAtions of both users and items via constructing homogeneous Graphs for multimOdal recommeNdation.
