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Distributed Design and Implementation of SVD++ Algorithm for E-commerce Personalized Recommender System

  • Jian Cao
  • Hengkui Hu
  • Tianyan Luo
  • Jia Wang
  • May Huang
  • Karl Wang
  • Zhonghai Wu
  • Xing Zhang
Part of the Communications in Computer and Information Science book series (CCIS, volume 572)

Abstract

Recommender systems can facilitate people to get effective information from the massive data, and it is the hot research currently in data mining. SVD++ is a kind of effective single model recommendation algorithm, which is based on the matrix decomposition combined with the neighborhood model. On the Spark, using the Stochastic Gradient Descent, this paper realized the distributed SVD++ algorithm through the Scala, deployed and applied the algorithm into an actual recommendation product for testing. The testing results represent that the distributed SVD++ algorithm succeeded in solving problems of terabytes of data processing in the e-commerce recommendation and the sparse data of user-item matrix, enhancing the quality in personalized commodity recommendation.

Keywords

Recommender system Matrix decomposition SVD++ Distributed computation E-commerce 

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Copyright information

© Springer Science+Business Media Singapore 2015

Authors and Affiliations

  • Jian Cao
    • 1
  • Hengkui Hu
    • 1
  • Tianyan Luo
    • 2
  • Jia Wang
    • 2
  • May Huang
    • 2
  • Karl Wang
    • 2
  • Zhonghai Wu
    • 1
  • Xing Zhang
    • 1
  1. 1.School of Software and MicroelectronicsPeking UniversityBeijingChina
  2. 2.Department of Electrical and Computer EngineeringInternational Technological UniversitySan JoseUSA

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