LSH-based collaborative recommendation method with privacy-preservation

Jiangmin XU, Xuansong LI, Hao WANG, Hong Ning DAI, Shunmei MENG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

Abstract

With the rapid development of cloud computing technology, massive services and online information cause information overload. Collaborative Filtering (CF) is one of the most successful and widely used technologies in personalized recommendation system to deal with information overload. However, traditional CF recommendation algorithms go through high time cost and poor real-time performance when dealing with the large-scale behavior data. Moreover, most collaborative recommendation methods mainly focus on improving recommendation accuracy, while ignore privacy preservation. In addition, the recommendation results of traditional CF recommendation algorithms are often too single, which could not meet user's diverse requirements. To solve these problems, this paper proposes a privacy-aware collaborative recommendation algorithm based on local sensitive hash (LSH) and factorization techniques. First, LSH is adopted to determine nearest neighbor set of the target users, where a neighbor matrix for the target user can be generated. The matrix factorization technique is applied in the neighbor matrix to predict the missing ratings. Then the nearest neighbors can be determined based on the predicted ratings. Finally, predictions for the target user are made based on the neighborhood-based CF recommendation model and diversified recommendations are made for the target user. Experimental results show that the proposed algorithm can effectively improve the efficiency of recommendation on the premise of protecting the privacy of users.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 13th International Conference on Cloud Computing, CLOUD 2020
PublisherIEEE Computer Society
Pages566-573
Number of pages8
ISBN (Electronic)9781728187808
DOIs
Publication statusPublished - Oct 2020
Externally publishedYes
Event13th IEEE International Conference on Cloud Computing, CLOUD 2020 - Virtual, Beijing, China
Duration: 18 Oct 202024 Oct 2020

Publication series

NameIEEE International Conference on Cloud Computing, CLOUD
Volume2020-October
ISSN (Print)2159-6182
ISSN (Electronic)2159-6190

Conference

Conference13th IEEE International Conference on Cloud Computing, CLOUD 2020
Country/TerritoryChina
CityVirtual, Beijing
Period18/10/2024/10/20

Bibliographical note

Funding Information:
ACKNOWLEDGEMENTS This paper is partially supported by the National Natural Science Foundation of China under Grant No. 61702264, No. 61702263, No. 61761136003, the Fundamental Research Funds for the Central Universities under Grant No. 30918014108, the Postdoctoral Science Foundation of China under Grant No. 2019M651835.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Cloud computing
  • Collaborative recommendation
  • Local sensitive hash
  • Matrix factorization

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