Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Discovering unexpected information from your competitors' web sites
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Modern Information Retrieval
Web mining from competitors' websites
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
CWS: a comparative web search system
Proceedings of the 15th 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
Fast direction-aware proximity for graph mining
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Competitor Mining with the Web
IEEE Transactions on Knowledge and Data Engineering
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Learning to infer social ties in large networks
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Cross-domain collaboration recommendation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Detecting competitive relationship plays a key role in a company's strategy formulation. Despite the importance, however, there are few automatic approaches to this problem, except several methods based on web data, which inevitably involve data noise. The challenging points of this problem are (1) to look for a set of data that can represent general information of the companies appropriately and (2) to figure out how underlying factors influence the competitive relationships between companies. In this paper, we choose patent records as data source and propose a novel approach to competitor detection problem. Specifically, we construct a patent network and employ an algorithm based on Random walk with restarts (RWR). Experimental results validate the of the algorithm. We further discuss to what extent the underlying factors influence the competitive relationship and finally demonstrate several representative results through case study.