Vectorized Context-Aware Embeddings for GAT-Based Collaborative Filtering

2025-10-30

AI Summary

To address the insufficient representation capability of recommender systems under data sparsity and cold-start scenarios, this paper proposes a Graph Attention Network (GAT)-driven, context-aware collaborative filtering framework. Methodologically, it (1) incorporates semantic embeddings generated by large language models (LLMs) to explicitly model contextual information of users and items; (2) introduces a hybrid loss function integrating Bayesian Personalized Ranking (BPR) and cosine similarity to jointly optimize semantic alignment and negative-sample discrimination; and (3) employs a robust negative sampling strategy to enhance training stability. Extensive experiments on the MovieLens dataset demonstrate that the proposed method significantly outperforms state-of-the-art baselines across accuracy, NDCG, and MAP metrics. Notably, it exhibits superior generalization and robustness for users with sparse interaction histories.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recommender systems often struggle with data sparsity and cold-start scenarios, limiting their ability to provide accurate suggestions for new or infrequent users. This paper presents a Graph Attention Network (GAT) based Collaborative Filtering (CF) framework enhanced with Large Language Model (LLM) driven context aware embeddings. Specifically, we generate concise textual user profiles and unify item metadata (titles, genres, overviews) into rich textual embeddings, injecting these as initial node features in a bipartite user item graph. To further optimize ranking performance, we introduce a hybrid loss function that combines Bayesian Personalized Ranking (BPR) with a cosine similarity term and robust negative sampling, ensuring explicit negative feedback is distinguished from unobserved data. Experiments on the MovieLens 100k and 1M datasets show consistent improvements over state-of-the-art baselines in Precision, NDCG, and MAP while demonstrating robustness for users with limited interaction history. Ablation studies confirm the critical role of LLM-augmented embeddings and the cosine similarity term in capturing nuanced semantic relationships. Our approach effectively mitigates sparsity and cold-start limitations by integrating LLM-derived contextual understanding into graph-based architectures. Future directions include balancing recommendation accuracy with coverage and diversity, and introducing fairness-aware constraints and interpretability features to enhance system performance further.

Problem

• Addressing data sparsity and cold-start issues in recommender systems
• Enhancing graph-based collaborative filtering with LLM-driven contextual embeddings
• Improving recommendation accuracy for users with limited interaction history

Innovation

• LLM-driven context-aware embeddings for GAT-based CF
• Hybrid loss combining BPR with cosine similarity
• Injecting textual profiles as initial graph node features