Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models

• URL: http://arxiv.org/abs/2508.04032v1
• Date: Wed, 06 Aug 2025 02:52:09 GMT
• Title: Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models
• Authors: Qian Yong, Yanhui Li, Jialiang Shi, Yaguang Dou, Tian Qi,
• Abstract summary: Large language models (LLMs) have demonstrated potential serendipity in recommendation, thanks to their extensive world knowledge and superior reasoning capabilities.<n>We propose a method that leverages llm to dynamically construct user knowledge graphs, thereby enhancing the serendipity of recommendation systems.
• Score: 0.9262403397108375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The feedback loop in industrial recommendation systems reinforces homogeneous content, creates filter bubble effects, and diminishes user satisfaction. Recently, large language models(LLMs) have demonstrated potential in serendipity recommendation, thanks to their extensive world knowledge and superior reasoning capabilities. However, these models still face challenges in ensuring the rationality of the reasoning process, the usefulness of the reasoning results, and meeting the latency requirements of industrial recommendation systems (RSs). To address these challenges, we propose a method that leverages llm to dynamically construct user knowledge graphs, thereby enhancing the serendipity of recommendation systems. This method comprises a two stage framework:(1) two-hop interest reasoning, where user static profiles and historical behaviors are utilized to dynamically construct user knowledge graphs via llm. Two-hop reasoning, which can enhance the quality and accuracy of LLM reasoning results, is then performed on the constructed graphs to identify users' potential interests; and(2) Near-line adaptation, a cost-effective approach to deploying the aforementioned models in industrial recommendation systems. We propose a u2i (user-to-item) retrieval model that also incorporates i2i (item-to-item) retrieval capabilities, the retrieved items not only exhibit strong relevance to users' newly emerged interests but also retain the high conversion rate of traditional u2i retrieval. Our online experiments on the Dewu app, which has tens of millions of users, indicate that the method increased the exposure novelty rate by 4.62%, the click novelty rate by 4.85%, the average view duration per person by 0.15%, unique visitor click through rate by 0.07%, and unique visitor interaction penetration by 0.30%, enhancing user experience.

Related papers

• DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic Reward [14.323631574821123]
  Model-based offline reinforcement learning has emerged as a promising approach for recommender systems.<n>DarLR is proposed to dynamically update world models to enhance recommendation policies.<n>Experiments on four benchmark datasets demonstrate the superior performance of DARLR.
  arXiv  Detail & Related papers  (2025-05-12T06:18:31Z)
• Multi-agents based User Values Mining for Recommendation [52.26100802380767]
  We propose a zero-shot multi-LLM collaborative framework for effective and accurate user value extraction.<n>We apply text summarization techniques to condense item content while preserving essential meaning.<n>To mitigate hallucinations, we introduce two specialized agent roles: evaluators and supervisors.
  arXiv  Detail & Related papers  (2025-05-02T04:01:31Z)
• Mind the Gap! Static and Interactive Evaluations of Large Audio Models [55.87220295533817]
  Large Audio Models (LAMs) are designed to power voice-native experiences.<n>This study introduces an interactive approach to evaluate LAMs and collect 7,500 LAM interactions from 484 participants.
  arXiv  Detail & Related papers  (2025-02-21T20:29:02Z)
• Retrieval Augmentation via User Interest Clustering [57.63883506013693]
  Industrial recommender systems are sensitive to the patterns of user-item engagement. We propose a novel approach that efficiently constructs user interest and facilitates low computational cost inference. Our approach has been deployed in multiple products at Meta, facilitating short-form video related recommendation.
  arXiv  Detail & Related papers  (2024-08-07T16:35:10Z)
• Knowledge Enhanced Multi-intent Transformer Network for Recommendation [11.53964363972865]
  We propose a novel approach named Knowledge Enhanced Multi-intent Transformer Network for Recommendation (KGTN) Global Intents with Graph Transformer focuses on capturing learnable user intents, by incorporating global signals from user-item-relation-entity interactions with a graph transformer. Knowledge Contrastive Denoising under Intents is dedicated to learning precise and robust representations.
  arXiv  Detail & Related papers  (2024-05-31T01:07:37Z)
• System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes [80.97898201876592]
  We propose a generative model in which past content interactions impact the arrival rates of users based on a self-exciting Hawkes process. We show analytically that given samples it is possible to disentangle System-1 and System-2 and allow content optimization based on user utility.
  arXiv  Detail & Related papers  (2024-05-29T18:19:37Z)
• Uncertainty-Aware Explainable Recommendation with Large Language Models [15.229417987212631]
  We develop a model that utilizes the ID vectors of user and item inputs as prompts for GPT-2. We employ a joint training mechanism within a multi-task learning framework to optimize both the recommendation task and explanation task. Our method achieves 1.59 DIV, 0.57 USR and 0.41 FCR on the Yelp, TripAdvisor and Amazon dataset respectively.
  arXiv  Detail & Related papers  (2024-01-31T14:06:26Z)
• Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction [97.99938802797377]
  Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in recommender systems. Recent deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. We propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper.
  arXiv  Detail & Related papers  (2022-06-28T03:28:15Z)
• Click-Through Rate Prediction Using Graph Neural Networks and Online Learning [0.0]
  A small percent improvement on the CTR prediction accuracy has been mentioned to add millions of dollars of revenue to the advertisement industry. This project is interested in building a CTR predictor using Graph Neural Networks and an online learning algorithm.
  arXiv  Detail & Related papers  (2021-05-09T01:35:49Z)
• Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach [115.76667128325361]
  Recommendation models can effectively estimate underlying user interests and predict one's future behaviors. We propose an inductive collaborative filtering framework that contains two representation models. Our model achieves promising results for recommendation on few-shot users with limited training ratings and new unseen users.
  arXiv  Detail & Related papers  (2020-07-09T14:31:25Z)

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.