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Title: A heterogeneous graph-based recommendation simulator

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 graph-based 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 Movie User Movie. The simulator then returns the recommendation results on the fly based on the user-customized semantics using a fast Monte Carlo algorithm.

Authors:
 [1];  [1];  [2];  [1]
  1. Seoul National University
  2. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
Work for Others (WFO)
OSTI Identifier:
1157174
DOE Contract Number:
DE-AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the 7th ACM conference on Recommender systems (RecSys 2013), Hongkong, China, 20131012, 20131016
Country of Publication:
United States
Language:
English

Citation Formats

Yeonchan, Ahn, Sungchan, Park, Lee, Matt Sangkeun, and Sang-goo, Lee. A heterogeneous graph-based recommendation simulator. United States: N. p., 2013. Web.
Yeonchan, Ahn, Sungchan, Park, Lee, Matt Sangkeun, & Sang-goo, Lee. A heterogeneous graph-based recommendation simulator. United States.
Yeonchan, Ahn, Sungchan, Park, Lee, Matt Sangkeun, and Sang-goo, Lee. Tue . "A heterogeneous graph-based recommendation simulator". United States. doi:.
@article{osti_1157174,
title = {A heterogeneous graph-based recommendation simulator},
author = {Yeonchan, Ahn and Sungchan, Park and Lee, Matt Sangkeun and Sang-goo, Lee},
abstractNote = {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 graph-based 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 Movie User Movie. The simulator then returns the recommendation results on the fly based on the user-customized semantics using a fast Monte Carlo algorithm.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 01 00:00:00 EST 2013},
month = {Tue Jan 01 00:00:00 EST 2013}
}

Conference:
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