A heterogeneous graph-based recommendation simulator

Authors:
Yeonchan Ahn;Sungchan Park;Sangkeun Lee;Sang-goo Lee
Affiliations:
Seoul National University, Seoul, South Korea;Seoul National University, Seoul, South Korea;Oak Ridge National Laboratory, Oak Ridge, TN, USA;Seoul National University, Seoul, South Korea
Venue:
Proceedings of the 7th ACM conference on Recommender systems
Year:
2013

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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.