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- W1972702102 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." @default.
- W1972702102 created "2016-06-24" @default.
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- W1972702102 date "2013-02-04" @default.
- W1972702102 modified "2023-10-16" @default.
- W1972702102 title "Patent partner recommendation in enterprise social networks" @default.
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- W1972702102 doi "https://doi.org/10.1145/2433396.2433404" @default.
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