TY - GEN
T1 - Patent partner recommendation in enterprise social networks
AU - Wu, Sen
AU - Sun, Jimeng
AU - Tang, Jie
PY - 2013/2/28
Y1 - 2013/2/28
N2 - 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.
AB - 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.
KW - cross collaboration
KW - predictive model
KW - social network
UR - http://www.scopus.com/inward/record.url?scp=84874281895&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874281895&partnerID=8YFLogxK
U2 - 10.1145/2433396.2433404
DO - 10.1145/2433396.2433404
M3 - Conference contribution
AN - SCOPUS:84874281895
SN - 9781450318693
T3 - WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining
SP - 43
EP - 52
BT - WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining
T2 - 6th ACM International Conference on Web Search and Data Mining, WSDM 2013
Y2 - 4 February 2013 through 8 February 2013
ER -