Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation

• URL: http://arxiv.org/abs/2507.21563v2
• Date: Wed, 06 Aug 2025 01:55:06 GMT
• Title: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation
• Authors: Minh-Anh Nguyen, Bao Nguyen, Ha Lan N. T., Tuan Anh Hoang, Duc-Trong Le, Dung D. Le,
• Abstract summary: Recommendation systems often suffer from data sparsity caused by limited user-item interactions.<n>This paper proposes a novel data augmentation framework that leverages Large Language Models and item textual descriptions.
• Score: 6.339905239860801
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommendation systems often suffer from data sparsity caused by limited user-item interactions, which degrade their performance and amplify popularity bias in real-world scenarios. This paper proposes a novel data augmentation framework that leverages Large Language Models (LLMs) and item textual descriptions to enrich interaction data. By few-shot prompting LLMs multiple times to rerank items and aggregating the results via majority voting, we generate high-confidence synthetic user-item interactions, supported by theoretical guarantees based on the concentration of measure. To effectively leverage the augmented data in the context of a graph recommendation system, we integrate it into a graph contrastive learning framework to mitigate distributional shift and alleviate popularity bias. Extensive experiments show that our method improves accuracy and reduces popularity bias, outperforming strong baselines.

Related papers

• PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems [1.0128808054306186]
  We propose PBiLoss, a regularization-based loss function designed to counteract popularity bias in graph-based recommender models explicitly.<n>We show that PBiLoss significantly improves fairness, as demonstrated by reductions in the Popularity-Rank Correlation for Users (PRU) and Popularity-Rank Correlation for Items (PRI)
  arXiv  Detail & Related papers  (2025-07-25T08:29:32Z)
• Balancing Semantic Relevance and Engagement in Related Video Recommendations [21.2575040646784]
  Related video recommendations commonly use collaborative filtering (CF) driven by co-engagement signals.<n>This paper introduces a novel multi-objective retrieval framework to balance semantic relevance and user engagement.
  arXiv  Detail & Related papers  (2025-07-12T21:04:25Z)
• Graph Contrastive Learning for Optimizing Sparse Data in Recommender Systems with LightGCL [0.0]
  LightGCL is a graph contrastive learning model that uses Singular Value Decomposition (SVD) for robust graph augmentation.<n>Our experiments also demonstrate improved fairness and resilience to popularity bias, making it well-suited for real-world recommender systems.
  arXiv  Detail & Related papers  (2025-05-28T17:21:41Z)
• Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering [16.02820746003461]
  graph contrastive learning (GCL) has gradually become a dominant approach in recommender systems.<n>In this paper, we reveal via theoretical derivation that the gradient descent process of the CL objective is formally equivalent to graph convolution.<n>We propose a novel neighborhood aggregation objective to bring users closer to all interacted items while pushing them away from other positive pairs.
  arXiv  Detail & Related papers  (2025-04-14T11:22:41Z)
• Training Large Recommendation Models via Graph-Language Token Alignment [53.3142545812349]
  We propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment.<n>By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs.<n> Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction.
  arXiv  Detail & Related papers  (2025-02-26T02:19:10Z)
• RAZOR: Sharpening Knowledge by Cutting Bias with Unsupervised Text Rewriting [16.633948320306832]
  biases prevalent in manually constructed datasets can introduce spurious correlations between tokens and labels.<n>Existing debiasing methods often rely on prior knowledge of specific dataset biases.<n>We propose RAZOR, a novel, unsupervised, and data-focused debiasing approach based on text rewriting for shortcut mitigation.
  arXiv  Detail & Related papers  (2024-12-10T17:02:58Z)
• Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization [75.1240295759264]
  We propose an effective framework for Bridging and Modeling Correlations in pairwise data, named BMC.<n>We increase the consistency and informativeness of the pairwise preference signals through targeted modifications.<n>We identify that DPO alone is insufficient to model these correlations and capture nuanced variations.
  arXiv  Detail & Related papers  (2024-08-14T11:29:47Z)
• 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)
• ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs [65.9625653425636]
  Large Language models (LLMs) exhibit harmful social biases. This work introduces a novel approach utilizing ChatGPT to generate synthetic training data.
  arXiv  Detail & Related papers  (2024-02-19T01:28:48Z)
• Ordinal Graph Gamma Belief Network for Social Recommender Systems [54.9487910312535]
  We develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions. OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences. We extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model.
  arXiv  Detail & Related papers  (2022-09-12T09:19:22Z)
• A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach [53.727460222955266]
  Temporal Sentence Grounding in Videos (TSGV) aims to ground a natural language sentence in an untrimmed video. Recent studies have found that current benchmark datasets may have obvious moment annotation biases. We introduce a new evaluation metric "dR@n,IoU@m" that discounts the basic recall scores to alleviate the inflating evaluation caused by biased datasets.
  arXiv  Detail & Related papers  (2022-03-10T08:58:18Z)

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.