Computer Science ›› 2020, Vol. 47 ›› Issue (11): 73-79.doi: 10.11896/jsjkx.200700088

Special Issue: Big Data & Data Scinece

• Database & Big Data & Data Science • Previous Articles     Next Articles

Temporal Reasoning Based Hierarchical Session Perception Recommendation Model

LUO Peng-yu1, WU Le1, LYU Yang2, YUAN Kun-ping3, HONG Ri-chang1   

  1. 1 School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230009,China
    2 School of Information Science and Technology,University of Science and Technology of China,Hefei 230027,China
    3 School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China
  • Received:2020-07-14 Revised:2020-08-23 Online:2020-11-15 Published:2020-11-05
  • About author:LUO Peng-yu,born in 1996,master student.His main research interests include data mining,recommendation system and machine learning.
    WU Le,born in 1988,Ph.D,associate professor.Her main research interests include recommendation systems,data mining and social network analysis.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61972125) and Fundamental Research Funds for the Central Universities (JZ2020HGPA0114).

Abstract: 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.

Key words: Anonymous session, Neural network, Session-based recommendation, Temporal reasoning, User's interest

CLC Number: 

  • TP311
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