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A Behavior-Item Based Hybrid Intention-Aware Frame for Sequence Recommendation

  • Yan Chen
  • Jiangwei Zeng
  • Haiping ZhuEmail author
  • Feng Tian
  • Yu Liu
  • Qidong Liu
  • Qinghua Zheng
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 41)

Abstract

Sequence recommendation is one of the hotspots of recommendation algorithm research. Most of the existing sequence recommendation methods focus on how to use the items’ attributes to characterize the user’s preferences, ignoring that the user behavior also can reflect the preference for items. However, user behavior often has problems of mis-interaction and random interaction, which leads to fully utilizing it difficultly. Therefore, this paper proposes a new Behavior-Item based Hybrid Intent-aware Framework (BIHIF). In this framework, the user’s main intent is extracted based on user behaviors and interactive items, respectively, the two intent vectors are combined and extracted by the full connection layer to obtain the user’s real intent. We use real intent and item vector to calculate the score of the candidate items and make Top-K recommendations. Based on the framework, we implement models respectively by MLP and GRU, which show good results in the experiments based on three real-world datasets.

Keywords

Sequence recommendation Hybrid Intention-aware User behavior Attention mechanism 

Notes

Acknowledgments

This work was supported by National Key Research and Development Program of China (2018YFB1004500), National Natural Science Foundation of China (61877048), Innovative Research Group of the National Natural Science Foundation of China (61721002), Innovation Research Team of Ministry of Education (IRT_17R86), Project of China Knowledge Centre for Engineering Science and Technology, the Natural Science Basic Research Plan in Shaanxi Province of China under Grant No. 2019JM-458.

We thank the anonymous reviewers for taking time to read and make valuable comments on this paper.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yan Chen
    • 1
  • Jiangwei Zeng
    • 1
  • Haiping Zhu
    • 1
    Email author
  • Feng Tian
    • 1
  • Yu Liu
    • 1
  • Qidong Liu
    • 1
  • Qinghua Zheng
    • 1
  1. 1.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina

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