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.
- D. Agarwal, B. Chen, P. Elango, R. Ramakrishnan, N. Motgi, S. Roy, and J. Zachariah. Online Models for Content Optimization. In NIPS, 2008.Google Scholar
- E. Agichtein, E. Brill, and S. Dumais. Improving Web Search Ranking by Incorporating User Behavior Information. In ACM SIGIR, pages 19--26, 2006. Google ScholarDigital Library
- J. Allan, R. Papka, and V. Lavrenko. On-line New Event Detection and Tracking. In ACM SIGIR, 1998. Google ScholarDigital Library
- C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to Rank using Gradient Descent. In ICML, 2005. Google ScholarDigital Library
- J. Callan. Distributed Information Retrieval. In Advances in Information Retrieval, pages 127--150, 2000.Google Scholar
- D. Carmel, E. Yom-Tov, A. Darlow, and D. Pelleg. What makes a Query Difficult? In ACM SIGIR, 2006. Google ScholarDigital Library
- D. Chakrabarti, D. Agarwal, and V. Josifovski. Contextual Advertising by Combining Relevance with Click Feedback. In WWW Conference, 2008. Google ScholarDigital Library
- G. M. D. Corso, A. Gull´1, and F. Romani. Ranking a Stream of News. In Proceedings of the 14th international conference on World Wide Web, pages 97--106, 2005. Google ScholarDigital Library
- F. Diaz. Integration of News Content into Web Results. In WSDM Conference, 2009. Google ScholarDigital Library
- J. Friedman. Greedy Function Approximation: a Gradient Boosting Machine. Annals of Statistics, 29(5), 2001.Google Scholar
- T. Jayram, S. Khot, R. Kumar, and Y. Rabani. Cell-Probe Lower Bounds for the Partial Match Problem. In STOC, 2003. Google ScholarDigital Library
- K. S. Jones, S. Walker, and S. E. Robertson. A probabilistic model of information retrieval: Development and comparative experiments. Information Processing and Management, 36(6), 2000. Google ScholarDigital Library
- A. C. König, K. Church, and M. Markov. A Data Struture for Sponsored Search. In IEEE ICDE Conference, 2009. Google ScholarDigital Library
- P. Li and K. W. Church. Using Sketches to Estimate Two-way and Multi-way Associations. Computational Linguistics, 33, 2007. Google ScholarDigital Library
- X. Li, Y.-Y. Wang, and A. Acero. Learning Query Intent from Regularized Click Graphs. In In Proc. ACM of SIGIR, 2008. Google ScholarDigital Library
- J. Lin. Divergence Measures based on the Shannon Entropy. IEEE Trans. on Information Theory, 37(1), 1991.Google Scholar
- C. Manning and H. Sch¨utze. Foundations of Statistical Natural Language Processing. MIT Press, 1999. Google ScholarDigital Library
- A. Maykov and M. Hurst. Social Streams Blog Crawler. In M3SN Workshop at IEEE ICDE Conference, 2009. Google ScholarDigital Library
- G. Navarro and V. M¨akinen. Compressed full-text indexes. ACM Comput. Surv., 39(1), 2007. Google ScholarDigital Library
- F. Radlinski and T. Joachims. Active Exploration for Learning Rankings from Clickthrough Data. In ACM SIKDD, 2007. Google ScholarDigital Library
- M. Regelson and D. Fain. Predicting Click-through Rate using Keyword Clusters. In 2nd Workshop on Sponsored Search Auctions, 2006.Google Scholar
- M. Richardson, E. Dominowska, and R. Ragno. Predicting Clicks: Estimating the Click-Through Rate for New Ads. In WWW Conference, pages 521--529, 2007. Google ScholarDigital Library
- Technorati. State of the Live Web. http://technorati.com/weblog/2007/04/328.html, April 2007.Google Scholar
- Technorati. State of the Blogosphere 2008. http://technorati.com/blogging/state-of-the-blogosphere/, 2008.Google Scholar
- M. West and J. Harrison. Bayesian Forecasting and Dynamic Models. Springer-Verlag, 1997. Google ScholarDigital Library
- Q. Wu, C. J. Burges, K. M. Svore, and J. Gao. Ranking, Boosting, and Model Adaptation. Technical report, Microsoft Research, 2008Google Scholar
Index Terms
Click-through prediction for news queries
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