GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Nearest Neighbor Search in High-Dimensional Space
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Object-level ranking: bringing order to Web objects
WWW '05 Proceedings of the 14th international conference on World Wide Web
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Objectrank: authority-based keyword search in databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Voronoi-based K nearest neighbor search for spatial network databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Fast algorithms for topk personalized pagerank queries
Proceedings of the 17th international conference on World Wide Web
Accuracy estimate and optimization techniques for SimRank computation
Proceedings of the VLDB Endowment
RankClus: integrating clustering with ranking for heterogeneous information network analysis
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Fast computation of SimRank for static and dynamic information networks
Proceedings of the 13th International Conference on Extending Database Technology
Fast query execution for retrieval models based on path-constrained random walks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Graph regularized transductive classification on heterogeneous information networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Axiomatic ranking of network role similarity
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
HeteRecom: a semantic-based recommendation system in heterogeneous networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining heterogeneous information networks: a structural analysis approach
ACM SIGKDD Explorations Newsletter
A proximity-based fallback model for hybrid web recommender systems
Proceedings of the 22nd international conference on World Wide Web companion
HeteroMF: recommendation in heterogeneous information networks using context dependent factor models
Proceedings of the 22nd international conference on World Wide Web
Measuring Relatedness Between Scientific Entities in Annotation Datasets
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Conventional research on similarity search focuses on measuring the similarity between objects with the same type. However, in many real-world applications, we need to measure the relatedness between objects with different types. For example, in automatic expert profiling, people are interested in finding the most relevant objects to an expert, where the objects can be of various types, such as research areas, conferences and papers, etc. With the surge of study on heterogeneous networks, the relatedness measure on objects with different types becomes increasingly important. In this paper, we study the relevance search problem in heterogeneous networks, where the task is to measure the relatedness of heterogeneous objects (including objects with the same type or different types). We propose a novel measure, called HeteSim, with the following attributes: (1) a path-constrained measure: the relatedness of object pairs are defined based on the search path that connect two objects through following a sequence of node types; (2) a uniform measure: it can measure the relatedness of objects with the same or different types in a uniform framework; (3) a semi-metric measure: HeteSim has some good properties (e.g., self-maximum and symmetric), that are crucial to many tasks. Empirical studies show that HeteSim can effectively evaluate the relatedness of heterogeneous objects. Moreover, in the query and clustering tasks, it can achieve better performances than conventional measures.