mlsanityData to 14 Oct 2026

Text Is All You Need: Learning Language Representations for Sequential Recommendation

arXiv 2305.13731 ↗First posted 23 May 2023cs.IRcited by 149

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Abstract (arXiv)

Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequence modeling to understand user preferences. While promising, these approaches still struggle to model cold-start items or transfer knowledge to new datasets. In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets. To this end, we present a novel framework, named Recformer, which effectively learns language representations for sequential recommendation. Specifically, we propose to formulate an item as a "sentence" (word sequence) by flattening item key-value attributes described by text so that an item sequence for a user becomes a sequence of sentences. For recommendation, Recformer is trained to understand the "sentence" sequence and retrieve the next "sentence". To encode item sequences, we design a bi-directional Transformer similar to the model Longformer but with different embedding layers for sequential recommendation. For effective representation learning, we propose novel pretraining and finetuning methods which combine language understanding and recommendation tasks. Therefore, Recformer can effectively recommend the next item based on language representations. Extensive experiments conducted on six datasets demonstrate the effectiveness of Recformer for sequential recommendation, especially in low-resource and cold-start settings.

Key facts. Cited by 149 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 May 2023.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 13; 2024: 56; 2025: 51; 2026: 29.

149 papers.

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  1. 2024Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendationscited by 188
  2. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  3. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  4. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 77
  5. 2024Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Effortscited by 74
  6. 2024HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modelingcited by 63
  7. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 53
  8. 2024IDGenRec: LLM-RecSys Alignment with Textual ID Learningcited by 53
  9. 2025Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendationcited by 51
  10. 2024Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender Systemcited by 50
  11. 2024eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Datacited by 46
  12. 2025A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applicationscited by 42
38 more
  1. 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representationscited by 35
  2. 2024Customizing Language Models with Instance-wise LoRA for Sequential Recommendationcited by 32
  3. 2023ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Predictioncited by 27
  4. 2023DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendationcited by 27
  5. 2024Rethinking Large Language Model Architectures for Sequential Recommendationscited by 27
  6. 2024Can Small Language Models be Good Reasoners for Sequential Recommendation?cited by 26
  7. 2024Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyondcited by 25
  8. 2024CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendationcited by 24
  9. 2024Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviorscited by 23
  10. 2024LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Applicationcited by 21
  11. 2024Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Modelscited by 20
  12. 2024Enhancing Recommendation Diversity by Re-ranking with Large Language Modelscited by 19
  13. 2024Stealthy Attack on Large Language Model based Recommendationcited by 19
  14. 2025LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendationcited by 19
  15. 2024Unifying Generative and Dense Retrieval for Sequential Recommendationcited by 18
  16. 2024Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendationcited by 17
  17. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  18. 2024Federated Recommendation via Hybrid Retrieval Augmented Generationcited by 16
  19. 2024LLMs for User Interest Exploration in Large-scale Recommendation Systemscited by 15
  20. 2024Taxonomy-Guided Zero-Shot Recommendations with LLMscited by 15
  21. 2025Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendationcited by 15
  22. 2025TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendationcited by 13
  23. 2023FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Predictioncited by 11
  24. 2024Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Modelcited by 11
  25. 2023Text Matching Improves Sequential Recommendation by Reducing Popularity Biasescited by 10
  26. 2024Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendationcited by 10
  27. 2024EasyRec: Simple yet Effective Language Models for Recommendationcited by 10
  28. 2024ID-Free Not Risk-Free: LLM-Powered Agents Unveil Risks in ID-Free Recommender Systemscited by 10
  29. 2024Context-Aware Clustering using Large Language Modelscited by 9
  30. 2024Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendationscited by 9
  31. 2024Preference Diffusion for Recommendationcited by 9
  32. 2025ChineseEcomQA: A Scalable E-commerce Concept Evaluation Benchmark for Large Language Modelscited by 9
  33. 2023LightLM: A Lightweight Deep and Narrow Language Model for Generative Recommendationcited by 8
  34. 2024FineRec:Exploring Fine-grained Sequential Recommendationcited by 8
  35. 2024Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanismcited by 8
  36. 2024STAR: A Simple Training-free Approach for Recommendations using Large Language Modelscited by 8
  37. 2024GraphAgent: Agentic Graph Language Assistantcited by 8
  38. 2025Lost in Sequence: Do Large Language Models Understand Sequential Recommendation?cited by 8

50 most cited of 149 All 149 (JSON)