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An E-commerce recommendation approach based on collaborative preferences extension clustering

  • School of Management, Harbin Institute of Technology
  • Heilongjiang University

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

Abstract

E-commerce recommendation helps consumers to find the products and services they want. Challenging research problems in E-commerce remain. The existing methods tend to use the same theme granularity. However due to the consumer's individual differences and the context of the consumer tasks, different consumers are not possible to understand all the same. Meanwhile, the data sparsity reduces the accuracy of the recommendation system. In this paper, we propose an approach on collaborative preferences extension based E-commerce recommendation that overcomes these drawbacks and try to find the hidden theme preferences, based on the collaborative extension SOM clustering method. We describes our method in three stages: collaborative preferences expansion, preference feature construction, and preferences clustering stage. Experiments show that the proposed approach is effective.

Original languageEnglish
Title of host publication2013 International Conference on Management Science and Engineering, ICMSE 2013 - 20th Annual Conference Proceedings
Pages51-56
Number of pages6
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 20th International Conference on Management Science and Engineering, ICMSE 2013 - Harbin, China
Duration: 17 Jul 201319 Jul 2013

Publication series

NameInternational Conference on Management Science and Engineering - Annual Conference Proceedings
ISSN (Print)2155-1847

Conference

Conference2013 20th International Conference on Management Science and Engineering, ICMSE 2013
Country/TerritoryChina
CityHarbin
Period17/07/1319/07/13

Keywords

  • E-commerce recommendation
  • collaborative preferences
  • feature extraction
  • preference feature construction

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