Scalable collaborative filtering based on efficient identification of similar users

Sang Chul Lee, Si Yong Lee, Dong Kyu Chae, Sang Wook Kim

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

Abstract

User-based collaborative filtering suffers from significant amount of computational overhead to find users similar to a target user. To reduce the overhead, we propose a novel method to identify unnecessary users and items in computing the similarity. Also, we propose a data structure to support the method quite efficiently. Through extensive experiments, we show the proposed method outperforms traditional methods up to 33.8 times.

Original languageEnglish
Title of host publicationProceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages210-213
Number of pages4
ISBN (Electronic)9781509012459
DOIs
StatePublished - 10 Jul 2017
Event5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016 - Beijing, China
Duration: 23 Sep 201625 Sep 2016

Publication series

NameProceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016

Conference

Conference5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016
Country/TerritoryChina
CityBeijing
Period23/09/1625/09/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

  • Collaborative filtering
  • Efficiency
  • Multidimensional indexing

Fingerprint

Dive into the research topics of 'Scalable collaborative filtering based on efficient identification of similar users'. Together they form a unique fingerprint.

Cite this