Research on personalized recommendation in E-commerce service based on data mining

Tao Xu, Jing Tian, Tomohiro Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We propose a new hybrid recommendation algorithm to optimization the cold-start problem with Collaborative Filtering (CF). And we use neighborhood-based collaborative filtering algorithm has obtained great favor due to simplicity, justifiability, and stability. However, when faced with large-scale, sparse, or noise affected data, nearest-neighbor collaborative filtering performs not so well, as the calculation of similarity between user or item pairs is costly and the accuracy of similarity can be easily affected by noise and sparsity. We introduce a new model comprising both In the training stage, user-item and film-item relationships in recommender systems, and describe how to use algorithm generates recommendations for cold-start items based on the preference model. Our experiments model provides a relatively efficient and accurate recommendation technique.

Original languageEnglish
Title of host publicationProceedings of the International MultiConference of Engineers and Computer Scientists 2013, IMECS 2013
PublisherNewswood Limited
Pages313-317
Number of pages5
ISBN (Print)9789881925183
Publication statusPublished - 2013 Jan 1
EventInternational MultiConference of Engineers and Computer Scientists 2013, IMECS 2013 - Kowloon, Hong Kong
Duration: 2013 Mar 132013 Mar 15

Publication series

NameLecture Notes in Engineering and Computer Science
Volume2202
ISSN (Print)2078-0958

Conference

ConferenceInternational MultiConference of Engineers and Computer Scientists 2013, IMECS 2013
CountryHong Kong
CityKowloon
Period13/3/1313/3/15

Keywords

  • Cold-s tart
  • Collaborative Filtering
  • Data mining
  • Data sparsity
  • Recommender system

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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  • Cite this

    Xu, T., Tian, J., & Murata, T. (2013). Research on personalized recommendation in E-commerce service based on data mining. In Proceedings of the International MultiConference of Engineers and Computer Scientists 2013, IMECS 2013 (pp. 313-317). (Lecture Notes in Engineering and Computer Science; Vol. 2202). Newswood Limited.