摘要
近年来,基于会话的推荐方法受到学术界的广泛关注。随着深度学习技术的不断发展,不同的模型结构被应用于基于会话的推荐方法中,如循环神经网络、注意力机制、图神经网络等。该文对这些基于会话的推荐模型进行了详细的分析、分类和对比,阐明了这些方法各自解决的问题与存在的不足。具体而言,该文首先通过调研,将基于会话的推荐方法与传统推荐方法进行比较,阐明基于会话的推荐方法的主要优缺点;其次,详细描述了现有的基于会话的推荐模型如何建模会话集中的复杂数据信息,以及这些模型方法可解决的技术问题;最后,该文讨论并指出了在基于会话推荐的领域中存在的挑战和未来研究的方向。
In recent years,session-based recommendation methods have attracted extensive attention from academics.With the continuous development of deep learning techniques,different model structures have been used in session-based recommendation methods,such as Recurrent Neural Networks,Attention Mechanism,and Graph Neural Networks.This paper conducts a detailed analysis,classification,and comparison over these models,and expounds on the target problems and shortcomings of these methods.In particular,this paper first compares the session-based recommendation methods with the traditional recommendation methods,and expounds the main advantages and disadvantages of the session-based recommendation methods through investigation.Subsequently,this paper details how complex data and information are modeled in session-based recommendation models,as well as the problems that these models can solve.Finally,this paper discusses and ideatifies the challenges and potential research directions in session-based recommendations.
作者
陈晋鹏
李海洋
张帆
李环
魏凯敏
CHEN Jinpeng;LI Haiyang;ZHANG Fan;LI Huan;WEI Kaimin(School of Computer Science(National Pilot Software Engineering School),Beijing University of Posts and Telecommunications,Beijing 100876,China;Key Laboratory of Trustworthy Distributed Computing and Service,Beijing University of Posts and Telecommunications,Beijing 100876,China;College of Computer Science and Technology,Zhejiang University,Hangzhou,Zhejiang 310058,China;College of Information Science and Technology,Jinan University,Guangzhou,Guangdong 510632,China)
出处
《中文信息学报》
CSCD
北大核心
2023年第3期1-17,26,共18页
Journal of Chinese Information Processing
基金
国家自然科学基金(61702043,61972178)
广东省自然科学基金(2019A1515011753,2019B1515120010)
关键词
基于会话的推荐方法
会话建模
深度学习
session-based recommendation method
session modeling
deep learning
作者简介
通信作者:陈晋鹏(1985—),博士,副教授,博士生导师,主要研究领域为社会媒体与数据挖掘、人工智能及应用。E-mail:jpchen@bupt.edu.cn;张帆(1997—),博士,主要研究领域为机器学习、推荐系统。E-mail:zhang_fan@bupt.edu.cn;李海洋(1996—),硕士,主要研究领域为机器学习、推荐系统。E-mail:haiyangli@bupt.edu.cn
