A Bidirectional Recommendation Method Research Based on Feature Transfer Learning

  • Yu Mao
  • Xiaozhong Fan
  • Fuquan ZhangEmail author
  • Sifan Zhang
  • Ke Niu
  • Hui Yang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)


In recommendation systems, data cold start is always an important problem to be solved. In this paper, aiming at problems such as few users, sparse evaluation data and difficulty of model start-up, a new bidirectional recommendation method based on feature transfer learning is proposed in the field of recommendation systems with two-way evaluation data. Based on the limited domain features, in order to transfer more useful information, we build a feature similarity based bridge between the target domain and the training field. First, we obtain the bidirectional recommendation matrix in the training field. Then, the feature space of users and items is vectorized to calculate the similarity between the target domain and the training domain. Finally, the feature transfer learning model is constructed to transfer the target domain, and the objective bidirectional recommendation matrix is obtained. The experimental results show that the method proposed in this paper can solve the data cold start problem in some bidirectional recommendation fields, and has achieved better results compared with the traditional recommendation method.


Bidirectional recommend Transfer learning Recommender system 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yu Mao
    • 1
  • Xiaozhong Fan
    • 1
  • Fuquan Zhang
    • 1
    • 2
    Email author
  • Sifan Zhang
    • 1
  • Ke Niu
    • 3
  • Hui Yang
    • 1
  1. 1.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina
  2. 2.Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University)FuzhouChina
  3. 3.Computer SchoolBeijing Information Science and Technology UniversityBeijingChina

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