Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanism

Jun Yin, Zhengxin Zeng, Mingzheng Li, Hao Yan, Chaozhuo Li, Weihao Han, Jianjin Zhang, Ruochen Liu, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang, Shirui Pan, Senzhang Wang

Track: User modeling, personalization and recommendation

Keywords: Sequential Recommender Systems, Large Language Models

Abstract: Owing to the unprecedented capability in semantic understanding and logical reasoning, the large language models (LLMs) have shown fantastic potential in developing the next-generation sequential recommender systems (RSs). However, on one hand, existing LLM-based sequential RSs mostly separate the index generation from the sequential recommendation, leading to insufficient integration between the semantic information and the collaborative information. On the other hand, the neglect of the user-related information hinders the LLM-based sequential RSs from exploiting the high-order user-item interaction patterns implicating in user behavior. In this paper, we propose the End-to-End Dual Dynamic (ED$^2$) recommender, the first LLM-based sequential recommender system which adopts the dual dynamic index mechanism, targeting at resolving the above limitations simultaneously. The dual dynamic index mechanism can not only assembly the index generation and the sequential recommendation into an unified LLM-backbone pipeline, but also make it practical for the LLM-based sequential recommender to take advantage of the user-related information. Specifically, to facilitate the LLMs comprehension ability to the dual dynamic index, we propose a multi-grained token regulator which constructs alignment supervision based on the LLMs semantic knowledge across multiple representation granularities. Moreover, the associated user collection data and a series of novel instruction tuning tasks are specially customized to exploit the user historical behavior in depth and capture the high-order user-item interaction patterns. Extensive experiments on three public datasets demonstrate the superiority of ED$^2$, achieving an average improvement of 19.41\% in Hit-Rate and 20.84\% in NDCG metric.

Submission Number: 482