mlsanityData to 7 Oct 2026

Text Matching Improves Sequential Recommendation by Reducing Popularity Biases

arXiv 2308.14029 ↗First posted 27 Aug 2023cs.IR cs.AIcited by 10

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Abstract (arXiv)

This paper proposes Text mAtching based SequenTial rEcommendation model (TASTE), which maps items and users in an embedding space and recommends items by matching their text representations. TASTE verbalizes items and user-item interactions using identifiers and attributes of items. To better characterize user behaviors, TASTE additionally proposes an attention sparsity method, which enables TASTE to model longer user-item interactions by reducing the self-attention computations during encoding. Our experiments show that TASTE outperforms the state-of-the-art methods on widely used sequential recommendation datasets. TASTE alleviates the cold start problem by representing long-tail items using full-text modeling and bringing the benefits of pretrained language models to recommendation systems. Our further analyses illustrate that TASTE significantly improves the recommendation accuracy by reducing the popularity bias of previous item id based recommendation models and returning more appropriate and text-relevant items to satisfy users. All codes are available at https://github.com/OpenMatch/TASTE.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 58 of its 74 references that are in the map. First posted on arXiv 27 Aug 2023.

Builds on

58 of 74 references.

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Cited by year

Citing papers per year: 2023: 1; 2024: 4; 2025: 5.

10 papers.

  1. 2024Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviorscited by 23
  2. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  3. 2024FineRec:Exploring Fine-grained Sequential Recommendationcited by 8
  4. 2025GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusioncited by 7
  5. 2024SPAR: Personalized Content-Based Recommendation via Long Engagement Attentioncited by 6
  6. 2024Disentangling ID and Modality Effects for Session-based Recommendationcited by 6
  7. 2023Modeling User Viewing Flow Using Large Language Models for Article Recommendationcited by 4
  8. 2025LLMsPark: A Benchmark for Evaluating Large Language Models in Strategic Gaming Contextscited by 4
  9. 2025ORBIT -- Open Recommendation Benchmark for Reproducible Research with Hidden Testscited by 1
  10. 2025Generate, Not Recommend: Personalized Multimodal Content Generationcited by 0