A Generative Approach for Alleviating Filter Bubbles in Collaborative Filtering

In addressing the filter bubble phenomenon in recommender systems, existing research has integrated large language models (LLMs) but faced distribution discrepancies and challenges like hallucinations and over-reliance on text data. We propose a two-stage method called LEAD (LLM-Enhanced Augmentation of Data) to mitigate these issues. First, we leverage LLMs' to extract textual representations of user interests and item audiences as auxiliary information. This information acts as pseudo-labels to guide a Conditional Generative Adversarial Network (CGAN) in generating unexpected items that align with actual user data distributions. Our approach harnesses model knowledge while avoiding data distribution gaps. Second, we introduce a heterogeneous view alignment strategy to align the semantic space of LLMs with the representation space of collaborative signals, enhancing the quality of representations and reducing noise. Extensive experiments on three real-world datasets demonstrate that LEAD outperforms existing models in recommendation quality, particularly in diversity and distribution alignment, showcasing the potential of LLMs in evolving recommender systems. Our code is available at https://github.com/gusuccc/LEAD.