Search Sciences | Yahoo! Labs

Web Search Sciences is responsible for constantly improving the search experience of Yahoo customers. Our scientists combine a diverse set of scientific disciplines, from information retrieval and machine learning to text- and data-mining, to create new algorithms and data models, for crawling, indexing, query and content understanding, ranking, and presenting results. Our scientists work with the engineering and product groups, and deliver innovation into Yahoo search impacting millions of users across the world, thousands of times every second.

Disciplines & Areas of Expertise

Scientific Disciplines include Information Retrival, Machine Learning, Data & Text Mining and Natural Language Processing. Learn More

Areas of Expertise include Ranking, Classification, Information Extraction and Summarization. Learn more

Team

Publications

Shape Classification Through Structured Learning of Matching Measures, Chen, Longbin, Turk M., McAuley J. J., Feris R. S., and Caetano T. S. , CVPR2009, 06/2009, Miami, FL, (2009)

Web Search Result Summarization: Title Selection Algorithms and User Satisfaction, Kanungo, Tapas, Vase Nadia(Ghamrawi), Kim Ki Yeun, and Wai Lawrenece , Conference on Information and Knowledge Management, November, 2009, Hong Kong, (2009) Abstract

Stochastic Gradient Boosting Distributed Decision Trees, Ye, Jerry, Chow Jyh-Herng, Chen Jiang, and Zheng Zhaohui , The 18th ACM Conference on Information and Knowledge Management (CIKM), 11/2009, Hong Kong, (2009)

Information Theoretic Regularization for Semi-Supervised Boosting, Lei, Zheng, Wang Shaojun, Yan Liu, and Lee Chi-Hoon , ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 06/2009, (2009)

Improving Web Page Classification by Label-propagation over Click Graphs, Kim, Soo-Min, Pantel Patrick, Duan Lei, and Gaffney Scott , Conference on Information and Knowledge Management (CIKM-2009), 11/2009, Hong Kong, (2009)

Threshold selection for web-page classification with highly skewed class distribution, He, Xiaofeng, Duan Lei, Zhou Yiping, and Dom Byron , World Wide Web 2009, Madrid, Spain, p.1081-1082, (2009)

The Dynamic Retrieval of XML Elements, Crouch, Carolyn, Khanna Sudip, Potnis Poorva, and Doddapenneni Nagendra , Advances in XML Information Retrieval and Evaluation, Volume Volume 39, (2006)

A dual coordinate descent method for large-scale linear SVM, Hsieh, Cho-Jui, Chang Kai-Wei, Lin Chih-Jen, Selvaraj Sathiya Keerthi, and Sellamanickam Sundararajan , ICML 2008, 2008, (2008)

Semi-Supervised Classification Using Sparse Gaussian Process Regression., Patel, Amrish, Sundararajan Sellamanickam, and Shevade Shirish , IJCAI 2009, 2009, (2009)

Network Flow for Collaborative Ranking, Zhuang, Ziming, Cucerzan Silviu, and Giles Lee , The 10th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 09/2006, Berlin, Germany, (2006)

Re-Ranking Search Results Using Query Logs, Zhuang, Ziming, and Cucerzan Silviu , The ACM 15th Conference on Information and Knowledge Management (CIKM), 11/2006, Arlington, VA, (2006)

Collaboration Over Time: Characterizing and Modeling Network Evolution, Huang, Jian, Zhuang Ziming, Li Jia, and Giles Lee , The First ACM International Conference on Web Search and Data Mining (WSDM), 02/2008, Stanford, CA, (2008)

Real-time Automatic Tag Recommendation, Song, Yang, Zhuang Ziming, Li Huajing, Zhao Qiankun, Lee Wang-Chien, Li Jia, and Giles Lee , The 31st Annual International ACM SIGIR Conference (SIGIR), 07/2008, Singapore, (2008)

Towards Click-based Models of Geographic Interests in Web Search, Zhuang, Ziming, Brunk Cliff, Mitra Prasenjit, and Giles Lee C. , ACM/IEEE/WIC International Conference on Web Intelligence (WI), 12/2008, Sydney, Australia, (2008)

Joint categorization of queries and clips for web-based video search, Zhang, Ruofei, Zhang Zhongfei, Sarukkai Ramesh, Chow Jyh-Herng, and Dai Wei , Multimedia Information Retrieval 2006, (2006)

Internet-scale collection of human-reviewed data, Su, Qi, Pavlov Dmitry, Chow Jyh-Herng, and Baker Wendell C. , WWW 2007, (2007)

Incorporating query difference for learning retrieval functions in world wide web search, Zha, H., Zheng Z., Fu H., and Sun G. , CIKM 2006 , (2006)

A Regression Framework for Learning ranking functions using relative relevance judgments, Zheng, Z., Zha H., Chen K., and Sun G. , SIGIR 2007, (2007)

A General Boosting Method and its Application to Learning Ranking Functions for Web Search, Chen, K., Zheng Z., Sun G., Zha H., Zhang T., and Chapelle O. , NIPS 2008, (2008)

Query-Level Learning to Rank Using Isotonic Regression, Zheng, Z., Zha H., and Sun G. , Proceedings of the 46th Annual Allerton Conference on Communication, Control and Computing 2008, (2008)

Enhancing Topical Ranking with Preferences from Click-Through Data, Chang, Y., Dong A., Liao C., and Zheng Z. , SIGIR 2009 poster , (2009)

Search Engine Adaptation by Feedback Control Adjustment for Time-sensitive Query, Zhang, R., Nie J., Chang Y., Zheng Z., and Metzler D. , NAACL-HLT 2009, (2009)

Web Search Engine Metrics: Direct Metrics to Measure User Satisfaction, Dasdan, A., Tsioutsiouliklis K., and Velipasaoglou E. , WWW (2009), Madrid, Spain, (2009)

Search result reranking by feedback control adjustment for time-sensitive query, Zhang, Ruiqiang, Nie Jian-yun, Chang Yi, Zheng Zhaohui, and Metzler Donald , HLT-NAACL 2009, (2009)

Document Preprocessing For Naive Bayes Classification and Clustering with Mixture of Multinomials, Pavlov, Dmitry, Parikh Jignashu, Balasubramanyan Ramnath, Dom Byron, and Kapur Shyam , Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD-2004), (2004)