# Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models

arXiv: https://arxiv.org/abs/2412.13544 · HTML: https://mlsanity.com/paper/2412.13544/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 46 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 28 of 28)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [GraphMAE: Self-Supervised Masked Graph Autoencoders](https://mlsanity.com/paper/2205.10803/) (2022)
- [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [Large Language Models are Zero-Shot Rankers for Recommender Systems](https://mlsanity.com/paper/2305.08845/) (2023)
- [Self-supervised Graph Learning for Recommendation](https://mlsanity.com/paper/2010.10783/) (2020)
- [Recommender Systems in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2307.02046/) (2023)
- [Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2112.08679/) (2021)
- [LLMRec: Large Language Models with Graph Augmentation for Recommendation](https://mlsanity.com/paper/2311.00423/) (2023)
- [A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions](https://mlsanity.com/paper/2109.12843/) (2021)
- [Knowledge Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1904.12575/) (2019)
- [Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction](https://mlsanity.com/paper/2305.06474/) (2023)
- [Learning Intents behind Interactions with Knowledge Graph for Recommendation](https://mlsanity.com/paper/2102.07057/) (2021)
- [Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences](https://mlsanity.com/paper/1902.06236/) (2019)
- [Improving Question Answering by Commonsense-Based Pre-Training](https://mlsanity.com/paper/1809.03568/) (2018)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Debiased Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2303.11780/) (2023)
- [Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System](https://mlsanity.com/paper/2306.09821/) (2023)
- [ProjE: Embedding Projection for Knowledge Graph Completion](https://mlsanity.com/paper/1611.05425/) (2016)
- [Rethinking Graph Masked Autoencoders through Alignment and Uniformity](https://mlsanity.com/paper/2402.07225/) (2024)
- [KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems](https://mlsanity.com/paper/1807.11141/) (2018)
- [Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph](https://mlsanity.com/paper/2402.13750/) (2024)
- [Factual and Informative Review Generation for Explainable Recommendation](https://mlsanity.com/paper/2209.12613/) (2022)
- [Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation](https://mlsanity.com/paper/2306.14462/) (2023)
- [Collaborative Recommendation Model Based on Multi-modal Multi-view Attention Network: Movie and literature cases](https://mlsanity.com/paper/2305.15159/) (2023)

## Built on by (most cited first, 6 of 6)

- [Improving LLM-powered Recommendations with Personalized Information](https://mlsanity.com/paper/2502.13845/) (2025)
- [Graph Foundation Models for Recommendation: A Comprehensive Survey](https://mlsanity.com/paper/2502.08346/) (2025)
- [WISER: Wider Search, Deeper Thinking, and Adaptive Fusion for Training-Free Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2602.23029/) (2026)
- [DCGL: Dual-Channel Graph Learning with Large Language Models for Knowledge-Aware Recommendation](https://mlsanity.com/paper/2605.07314/) (2026)
- [ANCHOR: Agentic Noise Creation Framework for Human Simulation and Denoising Recommendation](https://mlsanity.com/paper/2606.05621/) (2026)
- [Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion](https://mlsanity.com/paper/2606.05639/) (2026)

## Abstract (arXiv)

In recent years, knowledge graphs have been integrated into recommender systems as item-side auxiliary information, enhancing recommendation accuracy. However, constructing and integrating structural user-side knowledge remains a significant challenge due to the improper granularity and inherent scarcity of user-side features. Recent advancements in Large Language Models (LLMs) offer the potential to bridge this gap by leveraging their human behavior understanding and extensive real-world knowledge. Nevertheless, integrating LLM-generated information into recommender systems presents challenges, including the risk of noisy information and the need for additional knowledge transfer. In this paper, we propose an LLM-based user-side knowledge inference method alongside a carefully designed recommendation framework to address these challenges. Our approach employs LLMs to infer user interests based on historical behaviors, integrating this user-side information with item-side and collaborative data to construct a hybrid structure: the Collaborative Interest Knowledge Graph (CIKG). Furthermore, we propose a CIKG-based recommendation framework that includes a user interest reconstruction module and a cross-domain contrastive learning module to mitigate potential noise and facilitate knowledge transfer. We conduct extensive experiments on three real-world datasets to validate the effectiveness of our method. Our approach achieves state-of-the-art performance compared to competitive baselines, particularly for users with sparse interactions.