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Sequential Recommendation Based on Long-Term and Short-Term User Behavior with Self-attention

  • Xing Wei
  • Xianglin Zuo
  • Bo YangEmail author
Conference paper
  • 940 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11775)

Abstract

Product recommenders based on users’ interests are becoming increasingly essential in e-commerce. With the continuous development of the recommendation system, the available information is further enriched. In the case, user’s click or purchase behavior could be a visual representation of his or her interest. Due to the rapid update of products, users’ interests are not static, but change over time. In order to cope with the users’ interest changes, we propose a desirable work on the basis of representative recommendation algorithm. The sequence of user interaction behavior is thoroughly utilized, and the items that users interact at different times have different significance for the reflect of users’ interests. By considering the user’s sequential behaviors, this paper focuses on the recent ones to obtains the real interest of user. In this process, user behavior is divided into long-term and short-term, modeled by LSTM and Attention-based model respectively for user’s next click recommendation. We refer this model as LANCR and analyze the model in experiment. The experiment demonstrates that the proposed model has superior improvement compared with standard approaches. We deploy our model on two real datasets to verify the superior performance made in predicting user preferences.

Keywords

Recommender system Long short-term memory Sequential recommendation 

Notes

Acknowledgements

This work was supported in part by National Natural Science Foundation of China under grants 61876069 and 61572226, and Jilin Province Key Scientific and Technological Research and Development project under grants 20180201067GX and 20180201044GX.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Key Laboratory of Symbolic Computation and Knowledge Engineering Attached to the Ministry of EducationJilin UniversityChangchunChina

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