Predicting bounce rates in sponsored search advertisements

ABSTRACT

This paper explores an important and relatively unstudied quality measure of a sponsored search advertisement: bounce rate. The bounce rate of an ad can be informally defined as the fraction of users who click on the ad but almost immediately move on to other tasks. A high bounce rate can lead to poor advertiser return on investment, and suggests search engine users may be having a poor experience following the click. In this paper, we first provide quantitative analysis showing that bounce rate is an effective measure of user satisfaction. We then address the question, can we predict bounce rate by analyzing the features of the advertisement? An affirmative answer would allow advertisers and search engines to predict the effectiveness and quality of advertisements before they are shown. We propose solutions to this problem involving large-scale learning methods that leverage features drawn from ad creatives in addition to their keywords and landing pages.

REFERENCES

• 1: 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]
• 2: Eugene Agichtein , Eric Brill , Susan Dumais , Robert Ragno, Learning user interaction models for predicting web search result preferences, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148175]
• 3: Christopher M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag New York, Inc., Secaucus, NJ, 2006
• 4: Avrim Blum , Tom Mitchell, Combining labeled and unlabeled data with co-training, Proceedings of the eleventh annual conference on Computational learning theory, p.92-100, July 24-26, 1998, Madison, Wisconsin, United States [doi>10.1145/279943.279962]
• 5: Andrei Broder , Massimiliano Ciaramita , Marcus Fontoura , Evgeniy Gabrilovich , Vanja Josifovski , Donald Metzler , Vanessa Murdock , Vassilis Plachouras, To swing or not to swing: learning when (not) to advertise, Proceeding of the 17th ACM conference on Information and knowledge management, October 26-30, 2008, Napa Valley, California, USA [doi>10.1145/1458082.1458216]
• 6: Andrei Z. Broder , Peter Ciccolo , Marcus Fontoura , Evgeniy Gabrilovich , Vanja Josifovski , Lance Riedel, Search advertising using web relevance feedback, Proceeding of the 17th ACM conference on Information and knowledge management, October 26-30, 2008, Napa Valley, California, USA [doi>10.1145/1458082.1458217]
• 7: Andrei Broder , Marcus Fontoura , Vanja Josifovski , Lance Riedel, A semantic approach to contextual advertising, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277837]
• 8: B. Carterette and R. Jones. Evaluating search engines by modeling the relationship between relevance and clicks. In NIPS, 2007.
• 9: 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]
• 10: Massimiliano Ciaramita , Vanessa Murdock , Vassilis Plachouras, Online learning from click data for sponsored search, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367529]
• 11: S. Deerwester, S. Dumais, T. Landuaer, G. Furnas, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 1990.
• 12: O. Delalleau and Y. Bengio. Parallel stochastic gradient descent. In CIAR Summer School, Toronto, 2007.
• 13: W. A. Gale and G. Sampson. Good-turing frequency estimation without tears. J. of Quantitative Linguistics, 2:217--237, 1995.
• 14: Scott B. Huffman , Michael Hochster, How well does result relevance predict session satisfaction?, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277839]
• 15: Thorsten Joachims, Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms, Kluwer Academic Publishers, Norwell, MA, 2002
• 16: Thorsten Joachims , Laura Granka , Bing Pan , Helene Hembrooke , Geri Gay, Accurately interpreting clickthrough data as implicit feedback, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076063]
• 17: A. Kaushik. Bounce rate as sexiest web metric ever. MarketingProfs, August 2007. http://www.marketingprofs.com/7/bounce-rate-sexiest-web-metric-ever-kaushik.asp?sp=1.
• 18: A. Kaushik. Excellent analytics tip 11: Measure effectiveness of your web pages. Occam's Razor (blog), May 2007. http://www.kaushik.net/avinash/2007/05/excellent-analytics-tip-11-measure-effectiveness-of-your-web-pages.html.
• 19: H. J. Kushner and G. G. Yin. Stochastic Approximation Algorithms and Applications. Springer-Verlag, 1997.
• 20: J. Langford, L. Li, and T. Zhang. Sparse online learning via truncated gradient. In NIPS, 2008.
• 21: D. C. Liu , J. Nocedal, On the limited memory BFGS method for large scale optimization, Mathematical Programming: Series A and B, v.45 n.3, p.503-528, Dec. 1989 [doi>10.1007/BF01589116]
• 22: Vanessa Murdock , Massimiliano Ciaramita , Vassilis Plachouras, A noisy-channel approach to contextual advertising, Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising, p.21-27, August 12-12, 2007, San Jose, California [doi>10.1145/1348599.1348603]
• 23: Sandeep Pandey , Deepayan Chakrabarti , Deepak Agarwal, Multi-armed bandit problems with dependent arms, Proceedings of the 24th international conference on Machine learning, p.721-728, June 20-24, 2007, Corvalis, Oregon [doi>10.1145/1273496.1273587]
• 24: Benjamin Piwowarski , Hugo Zaragoza, Predictive user click models based on click-through history, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, November 06-10, 2007, Lisbon, Portugal [doi>10.1145/1321440.1321467]
• 25: J. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood models. In Advances in Large Margin Classifiers, pages 61--74. MIT Press, 1999.
• 26: Filip Radlinski , Andrei Broder , Peter Ciccolo , Evgeniy Gabrilovich , Vanja Josifovski , Lance Riedel, Optimizing relevance and revenue in ad search: a query substitution approach, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, July 20-24, 2008, Singapore, Singapore [doi>10.1145/1390334.1390404]
• 27: Berthier Ribeiro-Neto , Marco Cristo , Paulo B. Golgher , Edleno Silva de Moura, Impedance coupling in content-targeted advertising, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076119]
• 28: 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]
• 29: Bernhard Scholkopf , Alexander J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, MA, 2001
• 30: Shai Shalev-Shwartz , Yoram Singer , Nathan Srebro, Pegasos: Primal Estimated sub-GrAdient SOlver for SVM, Proceedings of the 24th international conference on Machine learning, p.807-814, June 20-24, 2007, Corvalis, Oregon [doi>10.1145/1273496.1273598]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.4 Applications

General Terms:
Experimentation

Keywords:
bounce rate, machine learning, online advertisement