基于时序推理的分层会话感知推荐模型  

Temporal Reasoning Based Hierarchical Session Perception Recommendation Model

作　　者：罗鹏宇 吴乐[1] 吕扬 袁堃平 洪日昌[1] LUO Peng-yu;WU Le;LYU Yang;YUAN Kun-ping;HONG Ri-chang(School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230009,China;School of Information Science and Technology,University of Science and Technology of China,Hefei 230027,China;School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China)

机构地区：[1]合肥工业大学计算机与信息学院,合肥230009 [2]中国科学技术大学信息科学技术学院,合肥230027 [3]北京邮电大学信息与通信工程学院,北京100876

出　　处：《计算机科学》2020年第11期73-79,共7页Computer Science

基　　金：国家自然科学基金(61972125);中央高校基本科研业务经费(JZ2020HGPA0114)。

摘　　要：基于会话的推荐系统,旨在根据匿名会话预测用户下一时刻的行为,这在很多互联网服务中颇为常见。该问题的主要挑战在于,如何模拟目标会话中用户行为的时序关系,并利用有限长度的会话刻画用户的兴趣。现有的方法根据目标会话中邻近物品的时序关系来建模用户的行为模式,并对目标会话中的物品信息进行选择性地保留和利用,进而聚合为会话的整体特征,并将其作为目标会话对应的用户兴趣。为了更好地建模用户行为模式和用户兴趣,文中提出了一种基于时序推理的分层会话感知推荐模型。一方面,不同于以往工作对目标会话中"邻近物品即相关"的假设,文中对目标会话中交互物品之间的依赖关系进行推理,并在会话中学习更灵活的时序关系,以建模用户的行为模式;另一方面,从目标会话中的物品和物品特征两个层次进行物品信息的聚合,实现更细粒度的用户兴趣推断。在两个公共数据集上的实验中,所提模型均优于其他基准模型,验证了其有效性。Session-based recommendation,which aims at predicting the user’s next action based on anonymous sessions,becomes a critical task in many online services.The main challenges of this problem are how to model the temporal relationship of user’s behaviors within the target session and capture user’s interest by the limited interactions.Existing methods model the user’s behavior patterns based on the temporal relationship of adjacent items within the target session,and aggregate the item information in the target session into overall session representation as the corresponding user’s interest.In order to improve these two processes,a novel Temporal Reasoning Based Hierarchical Session Perception Model(TRHSP)for session-based recommendation is proposed.On the one hand,unlike the previous works which assume adjacent items are related,TRHSP infers the dependency relationship between adjacent items in the target session and learns to handle the user-item interaction sequence with a flexible order,which helps to model user’s behavior.On the other hand,TRHSP aggregates the item information of the target session from both the item level and the item feature level,so as to capture user’s interest in a more fine-grained manner.In the experiments on two public datasets,the proposed TRHSP achieves the best performance,thus proving the effectiveness of the model.

关 键 词：基于会话的推荐系统 匿名会话 时序推理 用户兴趣 神经网络 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]