A heterogeneous graph-based recommendation simulator

Yeonchan Ahn, Sungchan Park, Sangkeun Lee, Sang Goo Lee

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

1 Scopus citations

Abstract

Heterogeneous graph-based recommendation frameworks have flexibility in that they can incorporate various recommendation algorithms and various kinds of information to produce better results. In this demonstration, we present a heterogeneous graphbased recommendation simulator which enables participants to experience the flexibility of a heterogeneous graph-based recommendation method. With our system, participants can simulate various recommendation semantics by expressing the semantics via meaningful paths like User → like Movie → liked by Movie The simulator then returns the recommendation results on the fly based on the user-customized semantics using a fast Monte Carlo algorithm.

Original languageEnglish
Title of host publicationRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
Pages471-472
Number of pages2
DOIs
StatePublished - 2013
Event7th ACM Conference on Recommender Systems, RecSys 2013 - Hong Kong, China
Duration: Oct 12 2013Oct 16 2013

Publication series

NameRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems

Conference

Conference7th ACM Conference on Recommender Systems, RecSys 2013
Country/TerritoryChina
CityHong Kong
Period10/12/1310/16/13

Keywords

  • Graph algorithm
  • Graph-based recommendation

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