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.
- L. Backstrom and J. Leskovec. Supervised random walks: predicting and recommending links in social networks. In WSDM'11, pages 635--644, 2011. Google ScholarDigital Library
- R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. ACM Press, 1999. Google ScholarDigital Library
- K. Balog, L. Azzopardi, and M. de Rijke. Formal models for expert finding in enterprise corpora. In SIGIR'2006, pages 43L. Backstrom and J. Leskovec. Supervised random walks: predicting and recommending links in social networks. In WSDM'11, pages 635--644, 2011.55, 2006. Google ScholarDigital Library
- D. J. Crandall, L. Backstrom, D. Cosley, S. Suri, D. Huttenlocher, and J. Kleinberg. Inferring social ties from geographic coincidences. PNAS, 107:22436--22441, Dec. 2010.Google ScholarCross Ref
- A. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: Scalable online collaborative filtering. In WWW'07, pages 271--280, 2007. Google ScholarDigital Library
- D. Easley and J. Kleinberg. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, 2010. Google ScholarCross Ref
- E. Gilbert and K. Karahalios. Predicting tie strength with social media. In CHI'09, pages 211--220, 2009. Google ScholarDigital Library
- J. M. Hammersley and P. Clifford. Markov field on finite graphs and lattices. Unpublished manuscript, 1971.Google Scholar
- P. Heymann and H. Garcia-Molina. Collaborative creation of communal hierarchical taxonomies in social tagging systems. Technical Report 2006-10, Stanford University, April 2006.Google Scholar
- J. E. Hopcroft, T. Lou, and J. Tang. Who will follow you back? reciprocal relationship prediction. In CIKM'11, pages 1137--1146, 2011. Google ScholarDigital Library
- X. Jin, S. Spangler, Y. Chen, K. Cai, R. Ma, L. Zhang, X. Wu, and J. Han. Patent maintenance recommendation with patent information network model. In ICDM'11, pages 280--289, 2011. Google ScholarDigital Library
- T. Joachims. Making large-Scale SVM Learning Practical. MIT-Press, 1999.Google Scholar
- H. Kautz, B. Selman, and M. Shah. Referral web: Combining social networks and collaborative filtering. Communications of the ACM, 40(3):63--65, 1997. Google ScholarDigital Library
- F. R. Kschischang, B. J. Frey, and H. Andrea Loeliger. Factor graphs and the sum-product algorithm. IEEE TOIT, 47:498--519, 2001.coincidences Google ScholarDigital Library
- T. Lappas, K. Liu, and E. Terzi. Finding a team of experts in social networks. In KDD'09, pages 467--476, 2009. Google ScholarDigital Library
- J. Leskovec, D. Huttenlocher, and J. Kleinberg. Predicting positive and negative links in online social networks. In WWW'10, pages 641--650, 2010. Google ScholarDigital Library
- D. Liben-Nowell and J. M. Kleinberg. The link-prediction problem for social networks. JASIST, 58(7):1019--1031, 2007. Google ScholarDigital Library
- Y. Liu, P.-y. Hseuh, R. Lawrence, S. Meliksetian, C. Perlich, and A. Veen. Latent graphical models for quantifying and predicting patent quality. In KDD'11, pages 1145--1153, 2011. Google ScholarDigital Library
- R. Mann. A new look at patent quality. American Law and Economics Association Annual Meetings, 2008.Google Scholar
- M. McPherson, L. Smith-Lovin, and J. Cook. Birds of a feather: Homophily in social networks. Annual review of sociology, pages 415--444, 2001.Google Scholar
- D. Mimno and A. McCallum. Expertise modeling for matching papers with reviewers. In KDD'07, pages 500--509, 2007. Google ScholarDigital Library
- M. Roth, A. Ben-David, D. Deutscher, G. Flysher, I. Horn, A. Leichtberg, N. Leiser, Y. Matias, and R. Merom. Suggesting friends using the implicit social graph. In KDD'10, pages 233--242, 2010. Google ScholarDigital Library
- D. Sculley. Combined regression and ranking. In KDD'10, pages 979--988, 2010. Google ScholarDigital Library
- Y. Shi, D. Ye, A. Goder, and S. Narayanan. A large scale machine learning system for recommending heterogeneous content in social networks. In SIGIR'11, pages 1337--1338, 2011. Google ScholarDigital Library
- J. Tang, T. Lou, and J. Kleinberg. Inferring social ties across heterogenous networks. In WSDM'12, pages 743--752, 2012. Google ScholarDigital Library
- J. Tang, B. Wang, Y. Yang, P. Hu, Y. Zhao, X. Yan, B. Gao, M. Huang, P. Xu, W. Li, and A. K. Usadi. Patentminer: topic-driven patent analysis and mining. In KDD '12, pages 1366--1374, 2012. Google ScholarDigital Library
- J. Tang, S. Wu, J. Sun, and H. Su. Cross-domain collaboration recommendation. In KDD'12, pages 1285--1293, 2012. Google ScholarDigital Library
- J. Tang, J. Zhang, R. Jin, Z. Yang, K. Cai, L. Zhang, and Z. Su. Topic level expertise search over heterogeneous networks. Machine Learning Journal, 82(2):211--237, 2011. Google ScholarDigital Library
- W. Tang, J. Tang, T. Lei, C. Tan, B. Gao, and T. Li. On optimization of expertise matching with various constraints. Neurocomputing, 76(1):71--83, 2012. Google ScholarDigital Library
- Y.-H. Tseng, C.-J. Lin, and Y.-I. Lin. Text mining techniques for patent analysis. Inf. Process. Manage., 43:1216--1247, September 2007. Google ScholarDigital Library
- J. S. Yedidia, W. T. Freeman, and Y. Weiss. Generalized belief propagation. In NIPS'01, pages 689--695, 2001.Google Scholar
- Q. Yuan, L. Chen, and S. Zhao. Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation. In RecSys'11, pages 245--252, 2011. Google ScholarDigital Library
- J. Zhang, J. Tang, and J. Li. Expert finding in a social network. In DASFAA'07, pages 1066--1069, 2007.Google ScholarCross Ref
Index Terms
- Patent partner recommendation in enterprise social networks
Recommendations
An effective recommendation method for cold start new users using trust and distrust networks
Recommendation systems analyze the purchasing behavior (e.g., item ratings) of users to learn about their preferences and recommend products or services that may be of interest to them. However, as new users require time to become familiar with ...
On top-k recommendation using social networks
RecSys '12: Proceedings of the sixth ACM conference on Recommender systemsRecommendation accuracy can be improved by incorporating trust relationships derived from social networks. Most recent work on social network based recommendation is focused on minimizing the root mean square error (RMSE). Social network based top-k ...
Learning and Transferring Social and Item Visibilities for Personalized Recommendation
CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge ManagementUser feedback in the form of movie-watching history, item ratings, or product consumption is very helpful in training recommender systems. However, relatively few interactions between items and users can be observed. Instances of missing user--item ...
Comments