The paper "Enhancing Sequential Recommendation via LLM-based Semantic Embedding Learning" by Jun Hu et al. introduces SAID, a framework that leverages large language models (LLMs) to learn semantically aligned item embeddings. The primary goal is to improve the performance of sequential recommendation systems (SRS) by preserving fine-grained semantic information from item textual descriptions. SAID employs a projector module to transform item IDs into embeddings, which are then fed into an LLM to elicit the exact descriptive text tokens. This process ensures that the learned embeddings capture the semantic meaning of the textual descriptions, enhancing the accuracy and efficiency of downstream sequential models. The framework is designed to be model-agnostic, allowing it to integrate with various lightweight sequential models such as GRU or Transformer. Experiments on six public datasets demonstrate that SAID outperforms baselines by 5% to 15% in terms of NDCG@10, and it has been successfully deployed in Alipay's online advertising platform, achieving a 3.07% improvement in cost per mile (CPM) with an online response time of under 20 milliseconds. The paper also discusses the impact of different LLMs on the quality of learned item embeddings and the importance of freezing item embeddings during downstream model training. Overall, SAID provides a practical and efficient solution for enhancing SRS performance by leveraging the capabilities of LLMs.The paper "Enhancing Sequential Recommendation via LLM-based Semantic Embedding Learning" by Jun Hu et al. introduces SAID, a framework that leverages large language models (LLMs) to learn semantically aligned item embeddings. The primary goal is to improve the performance of sequential recommendation systems (SRS) by preserving fine-grained semantic information from item textual descriptions. SAID employs a projector module to transform item IDs into embeddings, which are then fed into an LLM to elicit the exact descriptive text tokens. This process ensures that the learned embeddings capture the semantic meaning of the textual descriptions, enhancing the accuracy and efficiency of downstream sequential models. The framework is designed to be model-agnostic, allowing it to integrate with various lightweight sequential models such as GRU or Transformer. Experiments on six public datasets demonstrate that SAID outperforms baselines by 5% to 15% in terms of NDCG@10, and it has been successfully deployed in Alipay's online advertising platform, achieving a 3.07% improvement in cost per mile (CPM) with an online response time of under 20 milliseconds. The paper also discusses the impact of different LLMs on the quality of learned item embeddings and the importance of freezing item embeddings during downstream model training. Overall, SAID provides a practical and efficient solution for enhancing SRS performance by leveraging the capabilities of LLMs.