WebRec: Enhancing LLM-based Recommendations with Attention-guided RAG from Web

2025-11-18

AI Summary

Existing RAG-based recommender systems struggle to effectively leverage the Web—a dynamic, noisy external knowledge source—due to two key challenges: (1) a semantic gap between recommendation tasks and web retrieval, hindering precise user preference query generation; and (2) high noise and information sparsity in web content, impeding reliable signal extraction. To address these, we propose WebRec, the first framework that employs LLMs for end-to-end generation of structured, web-adapted queries. It introduces the MP-Head mechanism, which enhances long-range token interaction via message passing to improve attention modeling robustness against noise. Experiments on multiple recommendation benchmarks demonstrate that WebRec significantly improves both accuracy and information utilization efficiency—especially under low signal-to-noise ratio conditions. WebRec establishes a scalable, retrieval-augmented paradigm for open-domain recommendation.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Recommender systems play a vital role in alleviating information overload and enriching users' online experience. In the era of large language models (LLMs), LLM-based recommender systems have emerged as a prevalent paradigm for advancing personalized recommendations. Recently, retrieval-augmented generation (RAG) has drawn growing interest to facilitate the recommendation capability of LLMs, incorporating useful information retrieved from external knowledge bases. However, as a rich source of up-to-date information, the web remains under-explored by existing RAG-based recommendations. In particular, unique challenges are posed from two perspectives: one is to generate effective queries for web retrieval, considering the inherent knowledge gap between web search and recommendations; another challenge lies in harnessing online websites that contain substantial noisy content. To tackle these limitations, we propose WebRec, a novel web-based RAG framework, which takes advantage of the reasoning capability of LLMs to interpret recommendation tasks into queries of user preferences that cater to web retrieval. Moreover, given noisy web-retrieved information, where relevant pieces of evidence are scattered far apart, an insightful MP-Head is designed to enhance LLM attentions between distant tokens of relevant information via message passing. Extensive experiments have been conducted to demonstrate the effectiveness of our proposed web-based RAG methods in recommendation scenarios.

Problem

• Generating effective web queries for recommendation tasks
• Filtering noisy content from web-retrieved information
• Enhancing attention mechanisms for distant relevant evidence

Innovation

• Web-based RAG framework for LLM recommendations
• LLM-generated queries for web retrieval
• MP-Head attention mechanism for noisy web content