LLM-Empowered Representation Learning for Emerging Item Recommendation
This addresses the problem of recommending emerging items for users in domains like movies and pharmaceuticals, representing an incremental improvement over existing methods.
The paper tackles the challenge of recommending emerging items with gradually accumulating interactions by proposing EmerFlow, an LLM-empowered framework that generates distinctive embeddings, and it consistently outperforms existing methods across diverse domains.
In this work, we tackle the challenge of recommending emerging items, whose interactions gradually accumulate over time. Existing methods often overlook this dynamic process, typically assuming that emerging items have few or even no historical interactions. Such an assumption oversimplifies the problem, as a good model must preserve the uniqueness of emerging items while leveraging their shared patterns with established ones. To address this challenge, we propose EmerFlow, a novel LLM-empowered representation learning framework that generates distinctive embeddings for emerging items. It first enriches the raw features of emerging items through LLM reasoning, then aligns these representations with the embedding space of the existing recommendation model. Finally, new interactions are incorporated through meta-learning to refine the embeddings. This enables EmerFlow to learn expressive embeddings for emerging items from only limited interactions. Extensive experiments across diverse domains, including movies and pharmaceuticals, show that EmerFlow consistently outperforms existing methods.