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

arXiv: https://arxiv.org/abs/2508.04032 · HTML: https://mlsanity.com/paper/2508.04032/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- Builds on 38 of its 75 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Placed in

- LLM reasoning / [LLM reasoning for recommendation](https://mlsanity.com/lr/llm-reasoning-for-recommendation/)

## Builds on (most cited first, 30 of 38)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Solving Quantitative Reasoning Problems with Language Models](https://mlsanity.com/paper/2206.14858/) (2022)
- [Measuring and Narrowing the Compositionality Gap in Language Models](https://mlsanity.com/paper/2210.03350/) (2022)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [AI safety via debate](https://mlsanity.com/paper/1805.00899/) (2018)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://mlsanity.com/paper/1707.06690/) (2017)
- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://mlsanity.com/paper/2303.14524/) (2023)
- [Factuality Enhanced Language Models for Open-Ended Text Generation](https://mlsanity.com/paper/2206.04624/) (2022)
- [Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning](https://mlsanity.com/paper/1711.05851/) (2017)
- [Representation Learning with Large Language Models for Recommendation](https://mlsanity.com/paper/2310.15950/) (2023)
- [GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation](https://mlsanity.com/paper/2304.03879/) (2023)
- [PALR: Personalization Aware LLMs for Recommendation](https://mlsanity.com/paper/2305.07622/) (2023)
- [ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation](https://mlsanity.com/paper/2308.11131/) (2023)
- [Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models](https://mlsanity.com/paper/2305.13112/) (2023)
- [Approximate Nearest Neighbor Search in High Dimensions](https://mlsanity.com/paper/1806.09823/) (2018)
- [M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search](https://mlsanity.com/paper/1802.04394/) (2018)
- [Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights](https://mlsanity.com/paper/2305.15036/) (2023)
- [MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models](https://mlsanity.com/paper/2407.04960/) (2024)
- [Sliding Spectrum Decomposition for Diversified Recommendation](https://mlsanity.com/paper/2107.05204/) (2021)
- [Breaking Feedback Loops in Recommender Systems with Causal Inference](https://mlsanity.com/paper/2207.01616/) (2022)
- [CTRL: Connect Collaborative and Language Model for CTR Prediction](https://mlsanity.com/paper/2306.02841/) (2023)

## Abstract (arXiv)

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.