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Hybrid Recommendation Algorithm for E-Commerce Website

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Traditional recommendation algorithms face some serious problems, including data sparsity, cold start and inefficiency. To better address the problems above, the paper proposes a hybrid recommendation algorithm based on improved collaborative filtering of user context fuzzy clustering and content-based. For collaborative filtering, firstly, user classification is based on fuzzy clustering according to user context, and then collaborative filtering is used to recommend products for similar users. And the improved content-based algorithm sets up feature vectors for users and items dynamically. Experiments show that the hybrid algorithm can avoid defects of single algorithm and improve the performance in both recommendation quality and efficiency, which opens up exciting avenues for future research.

Original languageEnglish
Title of host publicationProceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages197-200
Number of pages4
ISBN (Electronic)9781467395861
DOIs
StatePublished - 11 May 2016
Externally publishedYes
Event8th International Symposium on Computational Intelligence and Design, ISCID 2015 - Hangzhou, Zhejiang, China
Duration: 12 Dec 201513 Dec 2015

Publication series

NameProceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015
Volume2

Conference

Conference8th International Symposium on Computational Intelligence and Design, ISCID 2015
Country/TerritoryChina
CityHangzhou, Zhejiang
Period12/12/1513/12/15

Keywords

  • Collaborative filtering
  • Content filtering
  • Hybrid recommendation

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