Papers with text-based recommendation

2 papers
RecGPT: Generative Pre-training for Text-based Recommendation (2024.acl-short)

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Challenge: Existing models for text-based recommendation lack data sparsity and flexibility to capture fluctuations in user preferences over time.
Approach: They present the first domain-adapted and fully-trained large language model for text-based recommendation.
Outcome: The proposed model outperforms baseline models on rating prediction and sequential recommendation tasks.
UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation (2023.acl-short)

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Challenge: Existing text-based recommendation frameworks that use pretrained language models (PLMs) can improve performance on text-related tasks.
Approach: They propose a unified local- and global-attention Transformer encoder to better model two-level contexts of user history.
Outcome: The proposed framework improves on three text-based recommendation tasks.

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