Sequential recommender system (SRS) predicts the next items that users may prefer based on user historical interaction sequences. Inspired by the rise of large language models (LLMs) in various AI applications, there is a surge of work on LLM-based SRS. Despite their attractive performance, existing LLM-based SRS still exhibit some limitations, including neglecting intra-item relations, ignoring long-term collaborative knowledge and using inflexible architecture designs for adaption. To alleviate these issues, we propose an LLM-based SRS named MixRec. Built on top of coarse-grained adaption for capturing inter-item relations, MixRec is further enhanced with (1) context masking that models intra-item relations to help LLM better understand token and item semantics in the context of SRS, (2) collaborative knowledge injection that helps LLM incorporate long-term collaborative knowledge, and (3) a dynamic adaptive mixture-of-experts design that can flexibly choose expert architectures based on Bayesian optimization to better incorporate different sequential information. Extensive experiments demonstrate that MixRec can effectively handle sequential recommendation in a dynamic and adaptive manner.

翻译：序列推荐系统（SRS）根据用户历史交互序列预测用户可能偏好的下一组物品。受大语言模型（LLM）在各种人工智能应用中兴起的启发，基于LLM的SRS研究激增。尽管现有基于LLM的SRS表现出诱人的性能，但仍存在一些局限性，包括忽略项内关系、忽视长期协同知识以及使用不灵活的适配架构设计。为缓解这些问题，我们提出一个名为MixRec的基于LLM的SRS。MixRec在捕获项间关系的粗粒度适配基础上，进一步通过以下方式增强：（1）上下文掩码建模项内关系，以帮助LLM在SRS上下文中更好地理解词元与物品语义；（2）协同知识注入帮助LLM融合长期协同知识；（3）动态自适应专家混合设计，能够基于贝叶斯优化灵活选择专家架构，以更好地融合不同的序列信息。大量实验表明，MixRec能以动态自适应的方式有效处理序列推荐任务。