近日，实验室张晓堃博士关于会话推荐的综述文章被数据挖掘顶级期刊IEEE Transaction on Knowledge and Data Engineering (TKDE)录用。TKDE是数据挖掘领域最权威的国际学术期刊之一，属于中国计算机学会CCF A类期刊，中科院一区期刊。

题目：A Survey on Side Information-driven Session-based Recommendation: From a Data-centric Perspective（

基于辅助信息的会话推荐研究综述：以数据为中心的视角）

摘要：Session-based recommendation is gaining increasing attention due to its practical value in predicting the intents of anonymous users based on limited behaviors. Emerging efforts incorporate various side information to alleviate inherent data scarcity issues in this task, leading to impressive performance improvements. The core of side information-driven session-based recommendation is the discovery and utilization of diverse data. In this survey, we provide a comprehensive review of this task from a data-centric perspective. Specifically, this survey commences with a clear formulation of the task. This is followed by a detailed exploration of various benchmarks rich in side information that are pivotal for advancing research in this field. Afterwards, we delve into how different types of side information enhance the task, underscoring data characteristics and utility. Moreover, we discuss the usage of various side information, including data encoding, data injection, and involved techniques. A systematic review of research progress is then presented, with the taxonomy by the types of side information. Finally, we summarize the current limitations and present the future prospects of this vibrant topic.

中文摘要：会话推荐旨在根据匿名用户在短时间内的行为为其推荐感兴趣的信息。由于其具有巨大实际应用价值，会话推荐受到了越来越多的关注。近年来，研究人员尝试引入多种辅助信息以缓解该任务中固有的数据稀疏问题。本文以数据为核心，对辅助信息驱动的会话推荐进行了全面综述。本文首先对该任务进行了清晰的定义。随后，详细介绍了多个包含丰富辅助信息的基准数据集。接着，深入探讨了不同类型的辅助信息如何提升会话推荐任务的表现，包括各类数据的特性与作用。此外，本文总结了现有方法对辅助信息的使用方式，包括数据编码的方式、数据注入的方式以及相关技术方法。在此基础上，本文按照辅助信息的类型对现有研究进展进行了系统性的梳理。最后，本文总结了该领域当前存在的主要挑战，并对该领域未来的发展方向进行了展望。