mlsanityData to 7 Oct 2026

Causality-Enhanced Behavior Sequence Modeling in LLMs for Personalized Recommendation

arXiv 2410.22809 ↗First posted 30 Oct 2024cs.IR cs.AIcited by 8

Not on the map. Why?

Abstract (arXiv)

Recent advancements in recommender systems have focused on leveraging Large Language Models (LLMs) to improve user preference modeling, yielding promising outcomes. However, current LLM-based approaches struggle to fully leverage user behavior sequences, resulting in suboptimal preference modeling for personalized recommendations. In this study, we propose a novel Counterfactual Fine-Tuning (CFT) method to address this issue by explicitly emphasizing the role of behavior sequences when generating recommendations. Specifically, we employ counterfactual reasoning to identify the causal effects of behavior sequences on model output and introduce a task that directly fits the ground-truth labels based on these effects, achieving the goal of explicit emphasis. Additionally, we develop a token-level weighting mechanism to adjust the emphasis strength for different item tokens, reflecting the diminishing influence of behavior sequences from earlier to later tokens during predicting an item. Extensive experiments on real-world datasets demonstrate that CFT effectively improves behavior sequence modeling. Our codes are available at https://github.com/itsmeyjt/CFT.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 31 of its 51 references that are in the map. First posted on arXiv 30 Oct 2024.

Builds on

31 of 51 references.

References · 31

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2018Self-Attentive Sequential Recommendationcited by 1,169
  3. 2017A Survey on Multi-Task Learningcited by 838
  4. 2015Session-based Recommendations with Recurrent Neural Networkscited by 685
  5. 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 473
  6. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  7. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 241
  8. 2023LLMRec: Large Language Models with Graph Augmentation for Recommendationcited by 171
  9. 2023Adapting Large Language Models by Integrating Collaborative Semantics for Recommendationcited by 171
  10. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  11. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  12. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
19 more
  1. 2021Causal Intervention for Leveraging Popularity Bias in Recommendationcited by 116
  2. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  3. 2024Learnable Item Tokenization for Generative Recommendationcited by 106
  4. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 101
  5. 2023CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendationcited by 99
  6. 2023Collaborative Large Language Model for Recommender Systemscited by 63
  7. 2021Deconfounded Recommendation for Alleviating Bias Amplificationcited by 51
  8. 2024IDGenRec: LLM-RecSys Alignment with Textual ID Learningcited by 50
  9. 2020Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issuecited by 48
  10. 2020A Critical Study on Data Leakage in Recommender System Offline Evaluationcited by 38
  11. 2024Harnessing Large Language Models for Text-Rich Sequential Recommendationcited by 38
  12. 2022Deconfounding Duration Bias in Watch-time Prediction for Video Recommendationcited by 37
  13. 2023Large Language Models for Intent-Driven Session Recommendationscited by 30
  14. 2023Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendationcited by 29
  15. 2024Text-like Encoding of Collaborative Information in Large Language Models for Recommendationcited by 25
  16. 2022Addressing Confounding Feature Issue for Causal Recommendationcited by 12
  17. 2024Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based Recommendationcited by 11
  18. 2024RecGPT: Generative Pre-training for Text-based Recommendationcited by 9
  19. 2023Dually Enhanced Propensity Score Estimation in Sequential Recommendationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2025SteerX: Disentangled Steering for LLM Personalizationcited by 7
  2. 2025IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendationcited by 5
  3. 2026Preference-Aware Rubric Learning for Personalized Evaluationcited by 2
  4. 2025Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenderscited by 1
  5. 2026BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Modelscited by 1
  6. 2025Personalized Causal Graph Reasoning for LLMs: An Implementation for Dietary Recommendationscited by 0
  7. 2025Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuningcited by 0
  8. 2026LBR: Towards Mitigating Length Bias in Large Language Models for Recommendationcited by 0