Counterfactual Tuning for Temporal Sensitivity Enhancement in Large Language Model-based Recommendation

• URL: http://arxiv.org/abs/2507.03047v1
• Date: Thu, 03 Jul 2025 10:11:35 GMT
• Title: Counterfactual Tuning for Temporal Sensitivity Enhancement in Large Language Model-based Recommendation
• Authors: Yutian Liu, Zhengyi Yang, Jiancan Wu, Xiang Wang,
• Abstract summary: Existing large language models (LLMs) fail to leverage the rich temporal information inherent in users' historical interaction sequences.<n>We propose Counterfactual Enhanced Temporal Framework for LLM-Based Recommendation (CETRec)<n> CETRec is grounded in causal inference principles, which allow it to isolate and measure the specific impact of temporal information on recommendation outcomes.
• Score: 8.798364656768657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances have applied large language models (LLMs) to sequential recommendation, leveraging their pre-training knowledge and reasoning capabilities to provide more personalized user experiences. However, existing LLM-based methods fail to sufficiently leverage the rich temporal information inherent in users' historical interaction sequences, stemming from fundamental architectural constraints: LLMs process information through self-attention mechanisms that lack inherent sequence ordering and rely on position embeddings designed primarily for natural language rather than user interaction sequences. This limitation significantly impairs their ability to capture the evolution of user preferences over time and predict future interests accurately. To address this critical gap, we propose Counterfactual Enhanced Temporal Framework for LLM-Based Recommendation (CETRec). CETRec is grounded in causal inference principles, which allow it to isolate and measure the specific impact of temporal information on recommendation outcomes. By conceptualizing temporal order as an independent causal factor distinct from item content, we can quantify its unique contribution through counterfactual reasoning--comparing what recommendations would be made with and without temporal information while keeping all other factors constant. This causal framing enables CETRec to design a novel counterfactual tuning objective that directly optimizes the model's temporal sensitivity, teaching LLMs to recognize both absolute timestamps and relative ordering patterns in user histories. Combined with our counterfactual tuning task derived from causal analysis, CETRec effectively enhances LLMs' awareness of both absolute order (how recently items were interacted with) and relative order (the sequential relationships between items).

Related papers

• When Relevance Meets Novelty: Dual-Stable Periodic Optimization for Exploratory Recommendation [6.663356205396985]
  Large language models (LLMs) demonstrate potential with their diverse content generation capabilities.<n>Existing LLM-enhanced dual-model frameworks face two major limitations.<n>First, they overlook long-term preferences driven by group identity, leading to biased interest modeling.<n>Second, they suffer from static optimization flaws, as a one-time alignment process fails to leverage incremental user data for closed-loop optimization.
  arXiv  Detail & Related papers  (2025-08-01T09:10:56Z)
• Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition [48.50019311384125]
  We explore simple and flexible prompt-based strategies that enable LLMs to perform time series forecasting without extensive retraining.<n>We propose our own method, PatchInstruct, which enables LLMs to make precise and effective predictions.
  arXiv  Detail & Related papers  (2025-06-15T19:42:58Z)
• Prioritizing Alignment Paradigms over Task-Specific Model Customization in Time-Series LLMs [24.25288301208334]
  This paper advocates a shift in approaching time-series reasoning with Large Language Models (LLMs)<n>It addresses the core limitations of current time-series reasoning approaches, which are often costly, inflexible, and inefficient.<n>We propose three alignment paradigms: Injective Alignment, Bridging Alignment and Internal Alignment.
  arXiv  Detail & Related papers  (2025-06-13T07:13:05Z)
• LLM-Symbolic Integration for Robust Temporal Tabular Reasoning [69.27153114778748]
  We introduce TempTabQA-C, a synthetic dataset designed for systematic and controlled evaluations.<n>This structured approach allows Large Language Models (LLMs) to generate and executesql queries, enhancing generalization and mitigating biases.
  arXiv  Detail & Related papers  (2025-06-06T05:14:04Z)
• Generative Recommendation with Continuous-Token Diffusion [11.23267167046234]
  We propose a novel framework for large language model (LLM)-based recommender systems (RecSys)<n>DeftRec incorporates textbfdenoising ditextbfffusion models to enable LLM-based RecSys to seamlessly support continuous textbftoken as input and target.<n>Given a continuous token as output, recommendations can be easily generated through score-based retrieval.
  arXiv  Detail & Related papers  (2025-04-16T12:01:03Z)
• Beyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation [83.87767101732351]
  Sequential recommender systems (SRS) predict the next items that users may prefer based on user historical interaction sequences. Inspired by the rise of large language models (LLMs) in various AI applications, there is a surge of work on LLM-based SRS. We propose DARec, a sequential recommendation model built on top of coarse-grained adaption for capturing inter-item relations.
  arXiv  Detail & Related papers  (2024-08-14T10:03:40Z)
• ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation [38.64175351885443]
  Large language models have been flourishing in the natural language processing (NLP) domain. Despite the intelligence shown by the recommendation-oriented finetuned models, LLMs struggle to fully understand the user behavior patterns. Existing works only fine-tune a sole LLM on given text data without introducing that important information to it.
  arXiv  Detail & Related papers  (2024-06-27T01:37:57Z)
• Improve Temporal Awareness of LLMs for Sequential Recommendation [61.723928508200196]
  Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks. LLMs fall short in recognizing and utilizing temporal information, rendering poor performance in tasks that require an understanding of sequential data. We propose three prompting strategies to exploit temporal information within historical interactions for LLM-based sequential recommendation.
  arXiv  Detail & Related papers  (2024-05-05T00:21:26Z)
• Data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteria [17.673512636899076]
  This study presents novel preference learning approaches to multiple criteria sorting problems in the presence of temporal criteria. To enhance scalability and accommodate learnable time discount factors, we introduce a novel monotonic Recurrent Neural Network (mRNN) The proposed mRNN can describe the preference dynamics by depicting marginal value functions and personalized time discount factors along with time.
  arXiv  Detail & Related papers  (2023-09-22T05:08:52Z)
• Learning Sequence Representations by Non-local Recurrent Neural Memory [61.65105481899744]
  We propose a Non-local Recurrent Neural Memory (NRNM) for supervised sequence representation learning. Our model is able to capture long-range dependencies and latent high-level features can be distilled by our model. Our model compares favorably against other state-of-the-art methods specifically designed for each of these sequence applications.
  arXiv  Detail & Related papers  (2022-07-20T07:26:15Z)
• Sequential Recommender via Time-aware Attentive Memory Network [67.26862011527986]
  We propose a temporal gating methodology to improve attention mechanism and recurrent units. We also propose a Multi-hop Time-aware Attentive Memory network to integrate long-term and short-term preferences. Our approach is scalable for candidate retrieval tasks and can be viewed as a non-linear generalization of latent factorization for dot-product based Top-K recommendation.
  arXiv  Detail & Related papers  (2020-05-18T11:29:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.