首页|Collaborative filtering recommendation algorithm based on interactive data classification

Collaborative filtering recommendation algorithm based on interactive data classification

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In the matrix factorization (MF) based collaborative filtering recommendation method,the most critical part is to deal with the interaction between the features of users and items.The mainstream approach is to use the inner product for MF to describe the user-item relationship.However,as a shallow model,MF has its limitations in describing the relationship between data.In addition,when the size of the data is large,the performance of MF is often poor due to data sparsity and noise.This paper presents a model called PIDC,short for potential interaction data clustering based deep learning recommendation.First,it uses classifiers to filter and cluster recommended items to solve the problem of sparse training data.Second,it combines MF and multi-layer perceptron (MLP) to optimize the prediction effect,and the limitation of inner product on the model expression ability is eliminated.The proposed model PIDC is tested on two datasets.The experimental results show that compared with the existing benchmark algorithm,the model improved the recommendation effect.

personalized recommendationdeep learningclusteringcollaborative filtering

Ji Yimu、Li Ke、Liu Shangdong、Liu Qiang、Yao Haichang、Li Kui

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School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telocommunicutions, Nanjing 210003, China

Jiangsu HPC and Intelligent Processing Engineer Research Center, Nanjing University of Posts and Telocommunicutions Nanjing 210003, China

Nanjing Center of HPC China, Nanjing University of Posts and Telocommunicutions, Nanjing 210003, China

Institue of High Performance Computing and Bigdata, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

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This work was supported by the National Key Research and Development Program of ChinaThis work was supported by the National Key Research and Development Program of ChinaNational Natural Science Foundation of China (61902194),the Outstanding Youth of Jiangsu Natural Science FoundationKey Research and Development Program of Jiangsu (BE2017166),and the Natural Science Foundation of the Jiangsu Higher Educati

2017YFB14013002017YFB1401301BK2017010019KJB520046

2020

10.19682/j.cnki.1005-8885.2020.0024

中国邮电高校学报(英文版)
北京邮电大学

中国邮电高校学报(英文版)

CSCDEI
影响因子：0.419
ISSN：1005-8885
年,卷(期)：2020.27(5)
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