Sequential recommender systems (SRS) predict 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 sequential recommendation model named DARec. Built on top of coarse-grained adaption for capturing inter-item relations, DARec 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 adaption mechanism that uses Bayesian optimization to flexibly choose layer-wise adapter architectures in order to better incorporate different sequential information. Extensive experiments demonstrate that DARec can effectively handle sequential recommendation in a dynamic and adaptive manner.

翻译：序列推荐系统（SRS）基于用户历史交互序列预测用户可能偏好的下一个项目。受大语言模型（LLM）在各种AI应用中兴起的启发，基于LLM的SRS研究激增。尽管性能引人注目，现有基于LLM的SRS仍存在一些局限性，包括忽略项目内关系、忽视长期协同知识以及使用不灵活的适应架构设计。为缓解这些问题，我们提出了一种名为DARec的基于LLM的序列推荐模型。在捕获项目间关系的粗粒度适应基础上，DARec通过以下方式进一步增强：(1) 上下文掩码建模项目内关系，以帮助LLM在SRS上下文中更好地理解标记和项目语义；(2) 协同知识注入帮助LLM融入长期协同知识；(3) 动态适应机制利用贝叶斯优化灵活选择分层适配器架构，以更好地融合不同的序列信息。大量实验表明，DARec能够以动态和自适应的方式有效处理序列推荐任务。