DynLLM: When Large Language Models Meet Dynamic Graph Recommendation
arxiv(2024)
摘要
Last year has witnessed the considerable interest of Large Language Models
(LLMs) for their potential applications in recommender systems, which may
mitigate the persistent issue of data sparsity. Though large efforts have been
made for user-item graph augmentation with better graph-based recommendation
performance, they may fail to deal with the dynamic graph recommendation task,
which involves both structural and temporal graph dynamics with inherent
complexity in processing time-evolving data. To bridge this gap, in this paper,
we propose a novel framework, called DynLLM, to deal with the dynamic graph
recommendation task with LLMs. Specifically, DynLLM harnesses the power of LLMs
to generate multi-faceted user profiles based on the rich textual features of
historical purchase records, including crowd segments, personal interests,
preferred categories, and favored brands, which in turn supplement and enrich
the underlying relationships between users and items. Along this line, to fuse
the multi-faceted profiles with temporal graph embedding, we engage LLMs to
derive corresponding profile embeddings, and further employ a distilled
attention mechanism to refine the LLM-generated profile embeddings for
alleviating noisy signals, while also assessing and adjusting the relevance of
each distilled facet embedding for seamless integration with temporal graph
embedding from continuous time dynamic graphs (CTDGs). Extensive experiments on
two real e-commerce datasets have validated the superior improvements of DynLLM
over a wide range of state-of-the-art baseline methods.
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