Paper Info
Title
HeteRecom: a semantic-based recommendation system in heterogeneous networks
Abstract
Making accurate recommendations for users has become an important function of e-commerce system with the rapid growth of WWW. Conventional recommendation systems usually recommend similar objects, which are of the same type with the query object without exploring the semantics of different similarity measures. In this paper, we organize objects in the recommendation system as a heterogeneous network. Through employing a path-based relevance measure to evaluate the relatedness between any-typed objects and capture the subtle semantic containing in each path, we implement a prototype system (called HeteRecom) for semantic based recommendation. HeteRecom has the following unique properties: (1) It provides the semantic-based recommendation function according to the path specified by users. (2) It recommends the similar objects of the same type as well as related objects of different types. We demonstrate the effectiveness of our system with a real-world movie data set.
Year
DOI
Venue
2012
10.1145/2339530.2339778
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Keywords
Field
DocType
semantic-based recommendation system,similar object,recommendation system,prototype system,different similarity measure,e-commerce system,important function,heterogeneous network,accurate recommendation,different type,semantic-based recommendation function,conventional recommendation system,semantic search,recommender system,e commerce,similarity
Recommender system,Semantic similarity,Data mining,Information retrieval,Semantic search,Computer science,Relevance measure,Heterogeneous network,Semantics
Conference
Citations 
PageRank 
References 
21
0.78
7
Authors
6
Name
Order
Citations
PageRank
Chuan Shi1113780.79
Chong Zhou2598.57
Xiangnan Kong3105957.66
Philip S. Yu422612.27
Gang Liu59836.92
Bai Wang649652.78