A Heterogeneous Graph-Based Recommendation Simulator


Title A Heterogeneous Graph-Based Recommendation Simulator
Yeonchan Ahn, Sungchan Park, Sangkeun Lee, Sang-goo Lee
Year 2013 / 10
Keywords Algorithms, Human Factors
Acknowledgement NRF
Publication Type International Conference
Publication Proceedings of the 7th ACM conference on Recommender systems (RecSys 2013), pp. 471-472
Link doi


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.