Enhancing Sequential Recommendation via LLM-based Semantic Embedding Learning

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.

In this paper, we introduce SAID, a framework that utilizes LLMs to explicitly learn Semantically Aligned item ID embeddings based on texts. For each item, SAID employs a projector module to transform an item ID into an embedding vector, which will be fed into an LLM to elicit the exact descriptive text tokens accompanied by the item. The item embeddings are forced to preserve fine-grained semantic information of textual descriptions. Further, the learned embeddings can be integrated with lightweight downstream sequential models for practical recommendations. In this way, SAID circumvents lengthy token sequences in previous works, reducing resources required in industrial scenarios and also achieving superior recommendation performance. Experiments on six public datasets demonstrate that SAID outperforms baselines by about 5% to 15% in terms of NDCG@10. Moreover, SAID has been deployed in Alipay's online advertising platform, achieving a 3.07% relative improvement of cost per mille (CPM) over baselines, with an online response time of under 20 milliseconds.