Journal on Communications ›› 2021, Vol. 42 ›› Issue (10): 130-139.doi: 10.11959/j.issn.1000-436x.2021185

• Papers • Previous Articles     Next Articles

Deep factorization machine model based on attention capsule

Yiran GU1,2, Zhupeng YAO1, Haigen YANG3   

  1. 1 College of Automation &College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    2 Center of Smart Campus Research, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    3 Center of Wider and Wireless Communication Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
  • Revised:2021-05-24 Online:2021-10-25 Published:2021-10-01
  • Supported by:
    The National Defense Basic Scientific Research Program of China(JCKY2019210B005);The National Defense Basic Scientific Research Program of China(JCKY2018204B025);The National Defense Basic Scientific Research Program of China(JCKY2017204B011);The Major National Defense Projects of China(ZQ2019D20401);The Simulation Pre Research Project of Equipment Development Department of China(41401030301)

Abstract:

Aiming at the problems of single feature combination of recommendation model, resolution of a large amount of valuable feature information, and over-fitting in deep learning, a new attentional scoring mechanism called attention capsule was designed, and a deep factorization machine model based on attention capsule was proposed.Users’ historical clicking and candidate items were processed through weight calculation based on the DeepFM model, reducing the impact of irrelevant features on the model, and the differential impact of different historical behaviors on users’ interests was fully explored.The adaptive regularization formulation was added to the training, which effectively reduced over-fitting without affecting the training speed.The comparison test on two public data sets shows that the proposed model is significantly enhanced in loss function and GAUC compared to other models.

Key words: recommendation model, deep learning, attention capsule, factorization machine, adaptive regularization

CLC Number: 

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