PathRank: a novel node ranking measure on a heterogeneous graph for recommender systems

Authors:
Sangkeun Lee;Sungchan Park;Minsuk Kahng;Sang-goo Lee
Affiliations:
Seoul National University, Seoul, South Korea;Seoul National University, Seoul, South Korea;Seoul National University, Seoul, South Korea;Seoul National University, Seoul, South Korea
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

Citing 9
Cited 2

Authoritative sources in a hyperlinked environment

Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
SimRank: a measure of structural-context similarity

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search

IEEE Transactions on Knowledge and Data Engineering
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
Temporal recommendation on graphs via long- and short-term preference fusion

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational retrieval using a combination of path-constrained random walks

Machine Learning
Performance of recommender algorithms on top-n recommendation tasks

Proceedings of the fourth ACM conference on Recommender systems
Random walk based entity ranking on graph for multidimensional recommendation

Proceedings of the fifth ACM conference on Recommender systems
A generic graph-based multidimensional recommendation framework and its implementations

Proceedings of the 21st international conference companion on World Wide Web

A proximity-based fallback model for hybrid web recommender systems

Proceedings of the 22nd international conference on World Wide Web companion
A heterogeneous graph-based recommendation simulator

Proceedings of the 7th ACM conference on Recommender systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a novel random-walk based node ranking measure, PathRank, which is defined on a heterogeneous graph by extending the Personalized PageRank algorithm. Not only can our proposed measure exploit the semantics behind the different types of nodes and edges in a heterogeneous graph, but also it can emulate various recommendation semantics such as collaborative filtering, content-based filtering, and their combinations. The experimental results show that PathRank can produce more various and effective recommendation results compared to existing approaches.