Memory Assisted LLM for Personalized Recommendation System

• URL: http://arxiv.org/abs/2505.03824v2
• Date: Mon, 03 Nov 2025 09:18:20 GMT
• Title: Memory Assisted LLM for Personalized Recommendation System
• Authors: Jiarui Chen,
• Abstract summary: Large language models (LLMs) have demonstrated significant potential in solving recommendation tasks.<n>We propose the Memory-Assisted Personalized LLM (MAP)<n>We first create a history profile for each user, capturing their preferences, such as ratings for historical items.<n>During recommendation, we extract relevant memory based on similarity, which is then incorporated into the prompts to enhance personalized recommendations.
• Score: 1.8788391002788387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated significant potential in solving recommendation tasks. With proven capabilities in understanding user preferences, LLM personalization has emerged as a critical area for providing tailored responses to individuals. Current studies explore personalization through prompt design and fine-tuning, paving the way for further research in personalized LLMs. However, existing approaches are either costly and inefficient in capturing diverse user preferences or fail to account for timely updates to user history. To address these gaps, we propose the Memory-Assisted Personalized LLM (MAP). Through user interactions, we first create a history profile for each user, capturing their preferences, such as ratings for historical items. During recommendation, we extract relevant memory based on similarity, which is then incorporated into the prompts to enhance personalized recommendations. In our experiments, we define a new task that enables testing with varying memory size under two scenarios: single domain where memory and tasks are from the same category and cross-domain (e.g. memory from movies and recommendation tasks in books). The results show that MAP outperforms regular LLM-based recommenders that integrate user history directly through prompt design. Moreover, as user history grows, MAP's advantage increases in both scenarios, making it more suitable for addressing successive personalized user requests.

Related papers

• Towards Realistic Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions [50.70965714314064]
  Large Language Models (LLMs) are increasingly serving as personal assistants, where users share complex and diverse preferences over extended interactions.<n>This work proposes RealPref, a benchmark for evaluating realistic preference-following in personalized user-LLM interactions.
  arXiv  Detail & Related papers  (2026-03-04T15:42:43Z)
• Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale [51.9706400130481]
  Large Language Models (LLMs) have emerged as personalized assistants for users across a wide range of tasks.<n> PERSONAMEM features curated user profiles with over 180 simulated user-LLM interaction histories.<n>We evaluate LLM chatbots' ability to identify the most suitable response according to the current state of the user's profile.
  arXiv  Detail & Related papers  (2025-04-19T08:16:10Z)
• ULMRec: User-centric Large Language Model for Sequential Recommendation [16.494996929730927]
  We propose ULMRec, a framework that integrates user personalized preferences into Large Language Models.<n>Extensive experiments on two public datasets demonstrate that ULMRec significantly outperforms existing methods.
  arXiv  Detail & Related papers  (2024-12-07T05:37:00Z)
• Optimizing Data Delivery: Insights from User Preferences on Visuals, Tables, and Text [59.68239795065175]
  We conduct a user study where users are shown a question and asked what they would prefer to see. We use the data to establish that a user's personal traits does influence the data outputs that they prefer.
  arXiv  Detail & Related papers  (2024-11-12T00:24:31Z)
• RAP: Retrieval-Augmented Personalization for Multimodal Large Language Models [53.304699445700926]
  We introduce the Retrieval Augmented Personalization framework for MLLMs' personalization.<n>Starting from a general MLLM, we turn it into a personalized assistant in three steps.<n>By pretraining on large-scale dataset, RAP-MLLMs can generalize to infinite visual concepts without additional finetuning.
  arXiv  Detail & Related papers  (2024-10-17T09:10:26Z)
• Aligning LLMs with Individual Preferences via Interaction [51.72200436159636]
  We train large language models (LLMs) that can ''interact to align''<n>We develop a multi-turn preference dataset containing 3K+ multi-turn conversations in tree structures.<n>For evaluation, we establish the ALOE benchmark, consisting of 100 carefully selected examples and well-designed metrics to measure the customized alignment performance during conversations.
  arXiv  Detail & Related papers  (2024-10-04T17:48:29Z)
• PersonalLLM: Tailoring LLMs to Individual Preferences [11.717169516971856]
  We present a public benchmark, PersonalLLM, focusing on adapting LLMs to provide maximal benefits for a particular user.<n>We curate open-ended prompts paired with many high-quality answers over which users would be expected to display heterogeneous latent preferences.<n>Our dataset and generated personalities offer an innovative testbed for developing personalization algorithms.
  arXiv  Detail & Related papers  (2024-09-30T13:55:42Z)
• LLMs + Persona-Plug = Personalized LLMs [41.60364110693824]
  Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their individual interests. This has led to the development of various personalized approaches aimed at adapting large language models (LLMs) to generate customized outputs aligned with user preferences. We propose a novel personalized LLM model, ours. It constructs a user-specific embedding for each individual by modeling all her historical contexts through a lightweight plug-in user embedder module.
  arXiv  Detail & Related papers  (2024-09-18T11:54:45Z)
• Reinforced Prompt Personalization for Recommendation with Large Language Models [24.360796133889156]
  We introduce the concept of instance-wise prompting, aiming at personalizing discrete prompts for individual users.<n>To improve efficiency and quality, RPP personalizes prompts at the sentence level rather than searching in the vast vocabulary word-by-word.
  arXiv  Detail & Related papers  (2024-07-24T09:24:49Z)
• A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles [55.951126447217526]
  We study personalized Task-oriented Dialogue Systems without assuming that user profiles are complete. We propose a Cooperative Memory Network (CoMemNN) that has a novel mechanism to gradually enrich user profiles. CoMemNN is able to enrich user profiles effectively, which results in an improvement of 3.06% in terms of response selection accuracy.
  arXiv  Detail & Related papers  (2021-02-16T18:05:54Z)
• MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation [46.0605442943949]
  A common challenge for most recommender systems is the cold-start problem. In this paper, we design two memory matrices that can store task-specific memories and feature-specific memories. We adopt a meta-optimization approach for optimizing the proposed method.
  arXiv  Detail & Related papers  (2020-07-07T03:25:15Z)
• 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.