Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation
CoRR(2024)
摘要
Conventional recommendation methods have achieved notable advancements by
harnessing collaborative or sequential information from user behavior.
Recently, large language models (LLMs) have gained prominence for their
capabilities in understanding and reasoning over textual semantics, and have
found utility in various domains, including recommendation. Conventional
recommendation methods and LLMs each have their strengths and weaknesses. While
conventional methods excel at mining collaborative information and modeling
sequential behavior, they struggle with data sparsity and the long-tail
problem. LLMs, on the other hand, are proficient at utilizing rich textual
contexts but face challenges in mining collaborative or sequential information.
Despite their individual successes, there is a significant gap in leveraging
their combined potential to enhance recommendation performance.
In this paper, we introduce a general and model-agnostic framework known as
Large language model with mutual augmentation and
adaptive aggregation for Recommendation (Llama4Rec).
Llama4Rec synergistically combines conventional and LLM-based recommendation
models. Llama4Rec proposes data augmentation and prompt augmentation strategies
tailored to enhance the conventional model and LLM respectively. An adaptive
aggregation module is adopted to combine the predictions of both kinds of
models to refine the final recommendation results. Empirical studies on three
real-world datasets validate the superiority of Llama4Rec, demonstrating its
consistent outperformance of baseline methods and significant improvements in
recommendation performance.
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