Abstract
In sponsored search, many studies focus on finding the most relevant advertisements (ads) and their optimal ranking for a submitted query. Determining whether it is suitable to show ads has received less attention. In this paper, we introduce the concept of user-aware advertisability, which refers to the probability of ad-click on sponsored ads when a specific user submits a query. When computing the advertisability for a given query-user pair, we first classify the clicked web pages based on a pre-defined category hierarchy and use the aggregated topical categories of clicked web pages to represent user preference. Taking user preference into account, we then compute the ad-click probability for this query-user pair. Compared with existing methods, the experimental results show that user preference is of great value for generating user-specific advertisability. In particular, our approach that computes advertisability per query-user pair outperforms the two state-of-the-art methods that compute advertisability per query in terms of a variant of the normalized Discounted Cumulative Gain metric.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ashkan, A., Clarke, C.L.A.: Term-based commercial intent analysis. In: Proceedings of the 32nd SIGIR, pp. 800–801 (2009)
Ashkan, A., Clarke, C.L.A.: Modeling browsing behavior for click analysis in sponsored search. In: Proceedings of the 21st CIKM, pp. 2015–2019 (2012)
Bennett, P.N., Nguyen, N.: Refined experts: improving classification in large taxonomies. In: Proceedings of the 32nd SIGIR, pp. 11–18 (2009)
Bennett, P.N., Svore, K., Dumais, S.T.: Classification-enhanced ranking. In: Proceedings of the 19th WWW, pp. 111–120 (2010)
Broder, A., Ciaramita, M., Fontoura, M., Gabrilovich, E., Josifovski, V., Metzler, D., Murdock, V., Plachouras, V.: To swing or not to swing: learning when (not) to advertise. In: Proceedings of the 17th CIKM, pp. 1003–1012 (2008)
Broder, A., Ciccolo, P., Gabrilovich, E., Josifovski, V., Metzler, D., Riedel, L., Yuan, J.: Online expansion of rare queries for sponsored search. In: Proceedings of the 18th WWW, pp. 511–520 (2009)
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: Proceedings of the 22nd ICML, pp. 89–96 (2005)
Cao, B., Sun, J.T., Xiang, E.W., Hu, D.H., Yang, Q., Chen, Z.: PQC: personalized query classification. In: Proceedings of the 18th CIKM, pp. 1217–1226 (2009)
Cheng, H., Cantú-Paz, E.: Personalized click prediction in sponsored search. In: Proceedings of the 3rd WSDM, pp. 351–360 (2010)
Cheng, Z., Gao, B., Liu, T.Y.: Actively predicting diverse search intent from user browsing behaviors. In: Proceedings of the 19th WWW, pp. 221–230 (2010)
Ciaramita, M., Murdock, V., Plachouras, V.: Online learning from click data for sponsored search. In: Proceedings of the 17th WWW, pp. 227–236 (2008)
CNET: Another engine takes ads by the click (1996), http://news.com.com/Anotherenginetakesadsbytheclick/2100-1033_3-212736.html
Dai, H.K., Zhao, L., Nie, Z., Wen, J.R., Wang, L., Li, Y.: Detecting online commercial intention (OCI). In: Proceedings of the 15th WWW, pp. 829–837 (2006)
Graepel, T., Candela, J.Q., Borchert, T., Herbrich, R.: Web-scale Bayesian click-through rate prediction for sponsored search advertising in Microsoft’s Bing search engine. In: Proceedings of the 27th ICML, pp. 13–20 (2010)
Hillard, D., Schroedl, S., Manavoglu, E., Raghavan, H., Leggetter, C.: Improving ad relevance in sponsored search. In: Proceedings of the 3rd WSDM, pp. 361–370 (2010)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems 20(4), 422–446 (2002)
Li, Y., Hsu, B.J.P., Zhai, C., Wang, K.: Unsupervised query segmentation using clickthrough for information retrieval. In: Proceedings of the 34th SIGIR, pp. 285–294 (2011)
Pandey, S., Punera, K., Fontoura, M., Josifovski, V.: Estimating advertisability of tail queries for sponsored search. In: Proceedings of the 33rd SIGIR, pp. 563–570 (2010)
Peng, F., Schuurmans, D.: Self-supervised chinese word segmentation. In: Hoffmann, F., Adams, N., Fisher, D., Guimarães, G., Hand, D.J. (eds.) IDA 2001. LNCS, vol. 2189, pp. 238–247. Springer, Heidelberg (2001)
Raghavan, H., Iyer, R.: Probabilistic first pass retrieval for search advertising: from theory to practice. In: Proceedings of the 19th CIKM, pp. 1019–1028 (2010)
Regelson, M., Fain, D.C.: Predicting click-through rate using keyword clusters. In: Electronic Commerce, EC (2006)
Tyler, L., Dávid, P., Martin, P.: Showing relevant ads via lipschitz context multi-armed bandits. In: Proceedings of the 13th AIS (2010)
Wang, L., Ye, M., Zou, Y.: A language model approach to capture commercial intent and information relevance for sponsored search. In: Proceedings of the 20th CIKM, pp. 599–604 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, HT., Sakai, T. (2013). User-Aware Advertisability. In: Banchs, R.E., Silvestri, F., Liu, TY., Zhang, M., Gao, S., Lang, J. (eds) Information Retrieval Technology. AIRS 2013. Lecture Notes in Computer Science, vol 8281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45068-6_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-45068-6_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45067-9
Online ISBN: 978-3-642-45068-6
eBook Packages: Computer ScienceComputer Science (R0)