Enhancing Sequential Recommendation via LLM-based Semantic Embedding Learning

Abstract

Sequential recommendation systems (SRS) are crucial in various applications as they enable users to discover relevant items based on their past interactions. Recent advancements involving large language models (LLMs) have shown significant promise in addressing intricate recommendation challenges. However, these efforts exhibit certain limitations. Specifically, directly extracting representations from an LLM based on items' textual features and feeding them into a sequential model hold no guarantee that the semantic information of texts could be preserved in these representations. Additionally, concatenating textual descriptions of all items in an item sequence into a long text and feeding it into an LLM for recommendation results in lengthy token sequences, which largely diminishes the practical efficiency.

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