Computer Science ›› 2019, Vol. 46 ›› Issue (10): 32-38.doi: 10.11896/jsjkx.180901801

• Big Data & Data Science • Previous Articles     Next Articles

Predicting User’s Dynamic Preference Based on Embedding Learning

WEN Wen1, LIN Ze-tian1, CAI Rui-chu1, HAO Zhi-feng1,2, WANG Li-juan1   

  1. (School of Computers,Guangdong University of Technology,Guangzhou 510000,China)1
    (School of Mathematics and Big Data,Foshan University,Foshan,Guangdong 528000,China)2
  • Received:2018-09-27 Revised:2019-03-14 Online:2019-10-15 Published:2019-10-21

Abstract: Traditional methods for capturing user preferences mainly focus on user’s long-term preferences.However,user interests always change over time in real-world applications.As a result,how to capture user’s dynamic prefe-rences still remains a big challenge.This paper proposed an embedding-based approach for predicting user’s dynamic preferences.Firstly,an improved embedding method is used for learning the low-dimensional vector representations of items from user’s click sequences.Then,based on the learned item vectors and user’s short-term click behaviors,user’sdynamic preferences are obtained and used for predicting the next click.Experiments were conducted on two real-world datasets and the proposed method was compared with state-of-the-art methods.The results demonstrate the significant superiority of the proposed method in prediction accuracy compared with other algorithms.

Key words: User preferences, Temporal behaviors, Behavior prediction, Embedding, Word2vec

CLC Number: 

  • TP181
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