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

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)

A structure-sensitive framework for text categorization, Ramakrishnan, Ganesh, Paranjpe Deepa, and Dom Byron , Proceedings of the 14th International Conference on Information and Knowledge Management (CIKM-2005), (2005)

Linear prediction models with graph regularization for web-page categorization, Zhang, Tong, Popescul Alexandrin, and Dom Byron , The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2006), (2006)

Yahoo! Answers: Applications of Machine Learning in Social Search, Dom, Byron , Proceedings of the ACM SIGIR 2007 Industry Event, (2007)

A Bayesian Technique for Estimating the Credibility of Question Answerers, Dom, Byron, and Paranjpe Deepa , Proceedings of the 2008 SIAM Conference on Data Mining (SDM08), (2008)

Global ranking by exploiting user clicks, Ji, Shihao, Zhou Ke, Liao Ciya, Zheng Zhaohui, Xue Gui-rong, Chapelle Olivier, Sun Gordon, and Zha Hongyuan , In Proceedings of The 32nd Annual ACM SIGIR Conference, 07/2009, Boston, MA, (2009)

Threshold Selection for Web-Page Classification with Highly Skewed Class Distribution, He, Xiaofeng, Duan Lei, Zhou Yiping, and Dom Byron , Proceedings of the 18th International World Wide Web Conference (WWW 2009), 04/2009, Madrid, Spain, (2009)

Context sensitive stemming for web search, Peng, Fuchun, Ahmed Nawaaz, Li Xin, and Lu Yumao , SIGIR, (2007)

Comparing Both Relevance and Robustness in Selection of Web Ranking Functions, Li, Fan, Li Xin, Ji Shihao, and Zheng Zhaohui , SIGIR Poster, (2009)

Web search engine evaluation using clickthrough data and a user model, Dupret, Georges, Murdock Vanessa, and Piwowarski Benjamin , In WWW2007 workshop Query Log Analysis: Social and Technological Challenges, 2007, (2007)

A study of mobile search queries in japan, Baeza-Yates, Ricardo, Dupret Georges, and Velasco Javier , In WWW2007 workshop Query Log Analysis: Social and Technological Challenges, 2007, (2007)

Enhancing educational-material retrieval using authored lesson metadata, Motelet, Olivier, Baloian Nelson, Piwowarski Benjamin, Dupret Georges, and Pino Jos A. , SPIRE 2007, (2007)

A user browsing model to predict search engine click data from past observations, Dupret, Georges, and Piwowarski Benjamin , In ACM Press, editor, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, (2008)

Threshold Selection for Web Page Classification with Highly Skewed Class Distribution, He, Xiaofeng, Duan Lei, Zhou Yiping, and Dom Byron , WWW, (2009)

An extension of precision-recall with user modelling (PRUM): Application to XML retrieval, Piwowarski, B., Gallinari P., and Dupret G. , Transactions on Information Systems (TOIS), (2007)