Patent partner recommendation in enterprise social networks

Sen Wu, Jimeng Sun, Jie Tang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It is often challenging to incorporate users' interactions into a recommendation framework in an online model. In this paper, we propose a novel interactive learning framework to formulate the problem of recommending patent partners into a factor graph model. The framework involves three phases: 1) candidate generation, where we identify the potential set of collaborators; 2) candidate refinement, where a factor graph model is used to adjust the candidate rankings; 3) interactive learning method to efficiently update the existing recommendation model based on inventors' feedback. We evaluate our proposed model on large enterprise patent networks. Experimental results demonstrate that the recommendation accuracy of the proposed model significantly outperforms several baselines methods using content similarity, collaborative filtering and SVM-Rank. We also demonstrate the effectiveness and efficiency of the interactive learning, which performs almost as well as offline re-training, but with only 1 percent of the running time.

Original languageEnglish (US)
Title of host publicationWSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining
Pages43-52
Number of pages10
DOIs
StatePublished - Feb 28 2013
Externally publishedYes
Event6th ACM International Conference on Web Search and Data Mining, WSDM 2013 - Rome, Italy
Duration: Feb 4 2013Feb 8 2013

Publication series

NameWSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining

Other

Other6th ACM International Conference on Web Search and Data Mining, WSDM 2013
CountryItaly
CityRome
Period2/4/132/8/13

Keywords

  • cross collaboration
  • predictive model
  • social network

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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