Click-through prediction for news queries

ABSTRACT

A growing trend in commercial search engines is the display of specialized content such as news, products, etc. interleaved with web search results. Ideally, this content should be displayed only when it is highly relevant to the search query, as it competes for space with "regular" results and advertisements. One measure of the relevance to the search query is the click-through rate the specialized content achieves when displayed; hence, if we can predict this click-through rate accurately, we can use this as the basis for selecting when to show specialized content. In this paper, we consider the problem of estimating the click-through rate for dedicated news search results. For queries for which news results have been displayed repeatedly before, the click-through rate can be tracked online; however, the key challenge for which previously unseen queries to display news results remains. In this paper we propose a supervised model that offers accurate prediction of news click-through rates and satisfies the requirement of adapting quickly to emerging news events.

REFERENCES

• 1: D. Agarwal, B. Chen, P. Elango, R. Ramakrishnan, N. Motgi, S. Roy, and J. Zachariah. Online Models for Content Optimization. In NIPS, 2008.
• 2: Eugene Agichtein , Eric Brill , Susan Dumais, Improving web search ranking by incorporating user behavior information, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148177]
• 3: James Allan , Ron Papka , Victor Lavrenko, On-line new event detection and tracking, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.37-45, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.290954]
• 4: Chris Burges , Tal Shaked , Erin Renshaw , Ari Lazier , Matt Deeds , Nicole Hamilton , Greg Hullender, Learning to rank using gradient descent, Proceedings of the 22nd international conference on Machine learning, p.89-96, August 07-11, 2005, Bonn, Germany [doi>10.1145/1102351.1102363]
• 5: J. Callan. Distributed Information Retrieval. In Advances in Information Retrieval, pages 127--150, 2000.
• 6: David Carmel , Elad Yom-Tov , Adam Darlow , Dan Pelleg, What makes a query difficult?, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148238]
• 7: Deepayan Chakrabarti , Deepak Agarwal , Vanja Josifovski, Contextual advertising by combining relevance with click feedback, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367554]
• 8: Gianna M. Del Corso , Antonio Gullí , Francesco Romani, Ranking a stream of news, Proceedings of the 14th international conference on World Wide Web, May 10-14, 2005, Chiba, Japan [doi>10.1145/1060745.1060764]
• 9: Fernando Diaz, Integration of news content into web results, Proceedings of the Second ACM International Conference on Web Search and Data Mining, February 09-12, 2009, Barcelona, Spain [doi>10.1145/1498759.1498825]
• 10: J. Friedman. Greedy Function Approximation: a Gradient Boosting Machine. Annals of Statistics, 29(5), 2001.
• 11: T. S. Jayram , Subhash Khot , Ravi Kumar , Yuval Rabani, Cell-probe lower bounds for the partial match problem, Proceedings of the thirty-fifth annual ACM symposium on Theory of computing, June 09-11, 2003, San Diego, CA, USA [doi>10.1145/780542.780639]
• 12: K. Sparck Jones , S. Walker , S. E. Robertson, A probabilistic model of information retrieval: development and comparative experiments, Information Processing and Management: an International Journal, v.36 n.6, p.779-808, Nov.06.2000 [doi>10.1016/S0306-4573(00)00015-7]
• 13: Arnd Christian König , Kenneth Church , Martin Markov, A Data Structure for Sponsored Search, Proceedings of the 2009 IEEE International Conference on Data Engineering, p.90-101, March 29-April 02, 2009 [doi>10.1109/ICDE.2009.37]
• 14: Ping Li , Kenneth W. Church, A Sketch Algorithm for Estimating Two-Way and Multi-Way Associations, Computational Linguistics, v.33 n.3, p.305-354, September 2007 [doi>10.1162/coli.2007.33.3.305]
• 15: Xiao Li , Ye-Yi Wang , Alex Acero, Learning query intent from regularized click graphs, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, July 20-24, 2008, Singapore, Singapore [doi>10.1145/1390334.1390393]
• 16: J. Lin. Divergence Measures based on the Shannon Entropy. IEEE Trans. on Information Theory, 37(1), 1991.
• 17: Christopher D. Manning , Hinrich Schütze, Foundations of statistical natural language processing, MIT Press, Cambridge, MA, 1999
• 18: Matthew Hurst , Alexey Maykov, Social Streams Blog Crawler, Proceedings of the 2009 IEEE International Conference on Data Engineering, p.1615-1618, March 29-April 02, 2009 [doi>10.1109/ICDE.2009.146]
• 19: Gonzalo Navarro , Veli Mäkinen, Compressed full-text indexes, ACM Computing Surveys (CSUR), v.39 n.1, p.2-es, 2007 [doi>10.1145/1216370.1216372]
• 20: Filip Radlinski , Thorsten Joachims, Active exploration for learning rankings from clickthrough data, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, August 12-15, 2007, San Jose, California, USA [doi>10.1145/1281192.1281254]
• 21: M. Regelson and D. Fain. Predicting Click-through Rate using Keyword Clusters. In 2nd Workshop on Sponsored Search Auctions, 2006.
• 22: Matthew Richardson , Ewa Dominowska , Robert Ragno, Predicting clicks: estimating the click-through rate for new ads, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242643]
• 23: Technorati. State of the Live Web. http://technorati.com/weblog/2007/04/328.html, April 2007.
• 24: Technorati. State of the Blogosphere 2008. http://technorati.com/blogging/state-of-the-blogosphere/, 2008.
• 25: Mike West , Jeff Harrison, Bayesian forecasting and dynamic models (2nd ed.), Springer-Verlag New York, Inc., New York, NY, 1997
• 26: Q. Wu, C. J. Burges, K. M. Svore, and J. Gao. Ranking, Boosting, and Model Adaptation. Technical report, Microsoft Research, 2008

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods

General Terms:
Algorithms, Experimentation, Performance

Keywords:
CTR prediction, click-through rates, news search