Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (9): 103-109.doi: 10.11896/jsjkx.200800129

Special Issue: Intelligent Data Governance Technologies and Systems

• Intelligent Data Governance Technologies and Systems • Previous Articles     Next Articles

Biased Deep Distance Factorization Algorithm for Top-N Recommendation

QIAN Meng-wei1 , GUO Yi 1,2,3   

  1. 1 School of Information Science and Engineering,East University of Science and Technology,Shanghai 200237,China
    2 Business Intelligence and Visualization Research Center,National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai 200436,China
    3 Shanghai Internet Big Data Engineering Technology Research Center,Shanghai 200072,China
  • Received:2020-08-20 Revised:2020-11-01 Online:2021-09-15 Published:2021-09-10
  • About author:QIAN Meng-wei,born in 1996,postgraduate.Her main research interests include data mining and recommender system.
    GUO Yi,born in 1975,Ph.D,professor.His main research interests include text mining,knowledge discovery and business intelligence.
  • Supported by:
    National Key Research and Development Program of China(2018YFC0807105),National Natural Science Foundation of China(61462073) and Science and Technology Committee of Shanghai Municipality(17DZ1101003,18511106602,18DZ2252300).

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Abstract: Since traditional matrix factorization algorithms are mostly based on shallow linear models,it is difficult to learn latent factors of users and items at a deep level.When the dataset is sparse,it is inclined to overfitting.To deal with the problem,this paper proposes a biased deep distance factorization algorithm,which can not only solve the data sparse problem,but also learn the distance feature vectors with stronger characterization capabilities.Firstly,the interaction matrix is constructed through the explicit and implicit data of the user and the item.Then the interaction matrix is converted into the corresponding distance matrix.Secondly,the distance matrix is input into the depth of the bias layer by row and column respectively.The neural network learns the distance feature vectors of users and items with non-linear features.Finally,the distance between the user and the item is calculated according to the distance feature vectors.Top-N item recommended list is generated according to the distance value.The experimental results show that Precision,Recall,MAP,MRR and NDCG of this algorithm are significantly improved compared to other mainstream recommendation algorithms on four different datasets.

Key words: Biased layer, Deep learning, Distance factorization, Item ranking

CLC Number: 

  • TP191
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