PathRank: Ranking nodes on a heterogeneous graph for flexible hybrid recommender systems

Information

Title PathRank: Ranking nodes on a heterogeneous graph for flexible hybrid recommender systems
Authors Sangkeun Lee, Sungchan Park, Minsuk Kahng, Sang-goo Lee
Year 2013 / 2
Keywords Heterogeneity, Graph, Hybrid, Recommender systems, Collaborative Filtering, Content-based Filtering, Context-awareness, Algorithms, Experimentation
Acknowledgement NRF
Publication Type International journal
Publication Expert Systems with Applications (ESWA), Volume 40, Issue 2, pp. 684-697
Index SCIE
Link doi

Abstract

We present a flexible hybrid recommender system that can emulate collaborative-filtering, Content-based Filtering, context-aware recommendation, and combinations of any of these recommendation semantics. The recommendation problem is modeled as a problem of finding the most relevant nodes for a given set of query nodes on a heterogeneous graph. However, existing node ranking measures cannot fully exploit the semantics behind the different types of nodes and edges in a heterogeneous graph. To overcome the limitation, we present a novel random walk based node ranking measure, PathRank, by extending the Personalized PageRank algorithm. The proposed measure can produce node ranking results with varying semantics by discriminating the different paths on a heterogeneous graph. The experimental results show that our method can produce more diverse and effective recommendation results compared to existing approaches.