Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (1): 115-120.doi: 10.11896/jsjkx.201200192

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

Convolutional Sequential Recommendation with Temporal Feature and User Preference

CHEN Jin-peng, HU Ha-lei, ZHANG Fan, CAO Yuan, SUN Peng-fei   

  1. School of Computer Science(National Pilot Software Engineering School),Beijing University of Posts and Telecommunications,Beijing 100876, China
  • Received:2020-12-22 Revised:2021-04-19 Online:2022-01-15 Published:2022-01-18
  • About author:CHEN Jin-peng,born in 1985,Ph.D,associate professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include social network analysis,recommendation system,data mining,and machine lear-ning.
  • Supported by:
    National Natural Science Foundation of China(61702043).

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Abstract: At present,recommendation system has been widely used in our life,which greatly facilitates people's life.The traditional recommendation method mainly analyzes the interaction between users and items and considers the history of users and items,and only obtains the user's preference for items in the past.The sequential recommendation system,by analyzing the interaction sequence of items in the recent period of time and considering the relevance between the user's previous and subsequent behaviors,can obtain user's preference for items in short term.It emphasizes the short-term connection between user and item,while ignoring the relationship between the attributes of the item.Aiming at the above problems,this paper presents a convolutional embedding recommendation with time and user preference (CERTU) model.This model can analyze the relations between items.It can obtain dynamic changes in user preferences.The model also considers the influence of individual item and multiple items to the next item.Experiments show that the performance of CERTU model is better than that of the current baseline method.

Key words: Convolutional neural network, Recommendation system, Sequential recommendation, Temporal feature, User preferences

CLC Number: 

  • TP311
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