Referral Web: combining social networks and collaborative filtering
Communications of the ACM
Modern Information Retrieval
Text mining techniques for patent analysis
Information Processing and Management: an International Journal
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Expertise modeling for matching papers with reviewers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Finding a team of experts in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Suggesting friends using the implicit social graph
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Combined regression and ranking
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Topic level expertise search over heterogeneous networks
Machine Learning
A large scale machine learning system for recommending heterogeneous content in social networks
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Latent graphical models for quantifying and predicting patent quality
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation
Proceedings of the fifth ACM conference on Recommender systems
Who will follow you back?: reciprocal relationship prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Patent Maintenance Recommendation with Patent Information Network Model
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Inferring social ties across heterogenous networks
Proceedings of the fifth ACM international conference on Web search and data mining
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Cross-domain collaboration recommendation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
PatentMiner: topic-driven patent analysis and mining
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.