ReFORM: Review-aggregated Profile Generation via LLM with Multi-Factor Attention for Restaurant Recommendation

• URL: http://arxiv.org/abs/2603.16236v1
• Date: Tue, 17 Mar 2026 08:22:17 GMT
• Title: ReFORM: Review-aggregated Profile Generation via LLM with Multi-Factor Attention for Restaurant Recommendation
• Abstract summary: We propose a ReFORM: Review-aggregated Profile Generation via LLM with Multi-FactOr Attentive RecoMmendation framework.<n>Specifically, we first generate factor-specific user and item profiles from reviews using LLM to capture a user's preference by items.<n>Then, we propose a Multi-Factor Attention to highlight the most influential factors in each user's decision-making process.
• Score: 1.0323063834827415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recommender systems, large language models (LLMs) have gained popularity for generating descriptive summarization to improve recommendation robustness, along with Graph Convolution Networks. However, existing LLM-enhanced recommendation studies mainly rely on the internal knowledge of LLMs about item titles while neglecting the importance of various factors influencing users' decisions. Although information reflecting various decision factors of each user is abundant in reviews, few studies have actively exploited such insights for recommendation. To address these limitations, we propose a ReFORM: Review-aggregated Profile Generation via LLM with Multi-FactOr Attentive RecoMmendation framework. Specifically, we first generate factor-specific user and item profiles from reviews using LLM to capture a user's preference by items and an item's evaluation by users. Then, we propose a Multi-Factor Attention to highlight the most influential factors in each user's decision-making process. In this paper, we conduct experiments on two restaurant datasets of varying scales, demonstrating its robustness and superior performance over state-of-the-art baselines. Furthermore, in-depth analyses validate the effectiveness of the proposed modules and provide insights into the sources of personalization. Our source code and datasets are available at https://github.com/m0onsoo/ReFORM.

Related papers

• Investigating LLM Variability in Personalized Conversational Information Retrieval [14.220276130333849]
  Mo et al. explored several strategies for incorporating Personal Textual Knowledge Bases (PTKB) into Large Language Models (LLMs)<n>We apply the original methods to a new TREC iKAT 2024 dataset and evaluate a diverse range of models, including Llama (1B-70B), Qwen-7B, GPT-4o-mini.<n>Our results show that human-selected PTKBs consistently enhance retrieval performance, while LLM-based selection methods do not reliably outperform manual choices.
  arXiv  Detail & Related papers  (2025-10-04T12:13:19Z)
• Learning to Shop Like Humans: A Review-driven Retrieval-Augmented Recommendation Framework with LLMs [30.748667156183004]
  RevBrowse is a review-driven recommendation framework inspired by the "browse-then-decide" decision process.<n>RevBrowse integrates user reviews into the LLM-based reranking process to enhance its ability to distinguish between candidate items.<n>PrefRAG is a retrieval-augmented module that disentangles user and item representations into structured forms.
  arXiv  Detail & Related papers  (2025-08-31T04:37:43Z)
• DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation [83.21140655248624]
  Large language models (LLMs) have been introduced into recommender systems (RSs)<n>We propose DeepRec, a novel LLM-based RS that enables autonomous multi-turn interactions between LLMs and TRMs for deep exploration of the item space.<n> Experiments on public datasets demonstrate that DeepRec significantly outperforms both traditional and LLM-based baselines.
  arXiv  Detail & Related papers  (2025-05-22T15:49:38Z)
• Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision [50.45597801390757]
  Instruct-LF is a goal-oriented latent factor discovery system.<n>It integrates instruction-following ability with statistical models to handle noisy datasets.
  arXiv  Detail & Related papers  (2025-02-21T02:03:08Z)
• LLM-based User Profile Management for Recommender System [15.854727020186408]
  PURE builds and maintains evolving user profiles by systematically extracting and summarizing key information from user reviews.<n>We introduce a continuous sequential recommendation task that reflects real-world scenarios by adding reviews over time and updating predictions incrementally.<n>Our experimental results on Amazon datasets demonstrate that PURE outperforms existing LLM-based methods.
  arXiv  Detail & Related papers  (2025-02-20T13:20:19Z)
• Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding [61.45448947483328]
  We introduce Lossless Acceleration via Speculative Decoding for LLM-based Recommender Systems (LASER)<n>LASER features a Customized Retrieval Pool to enhance retrieval efficiency and Relaxed Verification to improve the acceptance rate of draft tokens.<n>LASER achieves a 3-5x speedup on public datasets and saves about 67% of computational resources during the online A/B test.
  arXiv  Detail & Related papers  (2024-08-11T02:31:13Z)
• Few-shot Personalization of LLMs with Mis-aligned Responses [40.0349773257245]
  This paper proposes a new approach for a few-shot personalization of large language models (LLMs)<n>Our key idea is to learn a set of personalized prompts for each user by progressively improving the prompts using LLMs.<n>During an iterative process of prompt improvement, we incorporate the contexts of mis-aligned responses by LLMs.
  arXiv  Detail & Related papers  (2024-06-26T18:29:12Z)
• Unlocking the Potential of Large Language Models for Explainable Recommendations [55.29843710657637]
  It remains uncertain what impact replacing the explanation generator with the recently emerging large language models (LLMs) would have. In this study, we propose LLMXRec, a simple yet effective two-stage explainable recommendation framework. By adopting several key fine-tuning techniques, controllable and fluent explanations can be well generated.
  arXiv  Detail & Related papers  (2023-12-25T09:09:54Z)
• A Survey on Large Language Models for Recommendation [77.91673633328148]
  Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
  arXiv  Detail & Related papers  (2023-05-31T13:51:26Z)
• Sequential Recommendation with Self-Attentive Multi-Adversarial Network [101.25533520688654]
  We present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time.
  arXiv  Detail & Related papers  (2020-05-21T12:28:59Z)

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.