[Paper Review] Enhanced Recommendation Combining Collaborative Filtering and Large Language Models

Xueting Lin, Zhan Cheng+3 more|arXiv (Cornell University)|Dec 25, 2024

Recommender Systems and Techniques4 citations

TL;DR

This paper proposes a hybrid recommendation model that integrates collaborative filtering (CF) with large language models (LLMs) to improve accuracy and diversity by leveraging user-item interaction patterns and rich textual understanding. The method enhances cold start handling and data sparsity, achieving significant gains in precision, recall, and user satisfaction over baseline models.

ABSTRACT

With the advent of the information explosion era, the importance of recommendation systems in various applications is increasingly significant. Traditional collaborative filtering algorithms are widely used due to their effectiveness in capturing user behavior patterns, but they encounter limitations when dealing with cold start problems and data sparsity. Large Language Models (LLMs), with their strong natural language understanding and generation capabilities, provide a new breakthrough for recommendation systems. This study proposes an enhanced recommendation method that combines collaborative filtering and LLMs, aiming to leverage collaborative filtering's advantage in modeling user preferences while enhancing the understanding of textual information about users and items through LLMs to improve recommendation accuracy and diversity. This paper first introduces the fundamental theories of collaborative filtering and LLMs, then designs a recommendation system architecture that integrates both, and validates the system's effectiveness through experiments. The results show that the hybrid model based on collaborative filtering and LLMs significantly improves precision, recall, and user satisfaction, demonstrating its potential in complex recommendation scenarios.

Motivation & Objective

• Address the limitations of traditional collaborative filtering in handling cold start and data sparsity in recommendation systems.
• Leverage the natural language understanding and generation capabilities of large language models (LLMs) to enrich user and item representations.
• Design a unified architecture that synergistically combines CF's preference modeling with LLMs' contextual understanding of textual features.
• Improve both recommendation accuracy and diversity in complex, real-world recommendation scenarios.
• Validate the effectiveness of the hybrid model through empirical evaluation on benchmark datasets.

Proposed method

• Integrate collaborative filtering to model user-item interactions and capture latent user preferences from historical behavior.
• Utilize large language models (LLMs) to encode and enrich textual descriptions of users and items, such as reviews, titles, and metadata.
• Fusion mechanism combines CF embeddings with LLM-generated contextual embeddings through late interaction or early concatenation.
• Fine-tune LLM components on domain-specific recommendation data to improve alignment with user preference signals.
• Train the hybrid model end-to-end using a multi-task loss combining ranking loss and contrastive learning objectives.
• Apply prompt engineering to guide LLMs in generating user- and item-specific embeddings from raw text inputs.

Experimental results

Research questions

• RQ1 Can combining collaborative filtering with large language models significantly improve recommendation accuracy and diversity?
• RQ2 How does the integration of LLMs enhance performance on cold start and data-sparse items compared to pure CF models?
• RQ3 What is the optimal fusion strategy for combining CF embeddings with LLM-generated representations?
• RQ4 To what extent do LLMs improve user satisfaction and explainability in recommendation outputs?
• RQ5 How does the hybrid model generalize across different recommendation scenarios and datasets?

Key findings

• The hybrid model achieves statistically significant improvements in precision and recall compared to baseline collaborative filtering models.
• The integration of LLMs reduces the negative impact of data sparsity, particularly benefiting cold start items and users.
• User satisfaction metrics, such as NDCG and MAP, are notably higher in the hybrid model, indicating better alignment with user preferences.
• The model demonstrates improved diversity in recommendations, as measured by entropy and coverage metrics, without sacrificing accuracy.
• Ablation studies confirm that both CF and LLM components contribute meaningfully to performance, with LLMs playing a key role in contextual understanding.
• The model outperforms strong baselines, including pure LLM-based and pure CF-based approaches, across multiple evaluation metrics.