Abstract
In the real-time bidding (RTB) display advertising ecosystem, demand-side-platforms (DSPs) buy ad impressions through real-time auction or bidding from ad exchanges for advertisers. Receiving a bid request, DSP needs predict the click through rate (CTR) for ads and determine whether to bid and calculates the bid price according to the CTR estimated. In this paper, we address CTR estimation in DSP as a recommendation issue. Due to the complicated trilateral interactions among users, ads and publishers (web pages), conventional matrix factorization does not perform well. Adopting ideas from high-order singular value decomposition (HOSVD), we extend two dimensional matrix factorization model to three dimensional cube factorization containing users, ads and publishers, and propose an improved cube factorization model to address it. We evaluate its performance over a real-world advertising dataset and the results demonstrate that the improved cube factorization model outperforms the matrix factorization.
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Lee, K., Orten, B.B., Dasdan, A., et al.: Estimating conversion rate in display advertising from past performance data: U.S. Patent Application 13/584, 545[P] (2012)
Chen, Y., Berkhin, P., Anderson, B., Devanur, N.R.: Real-time bidding algorithms for performance-based display ad allocation. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1307–1315. ACM (August 2011)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender sys-tems. Computer 42(8), 30–37 (2009)
Chen, T., Tang, L., Liu, Q., Yang, D., Xie, S., Cao, X., Wu, C., Yao, E., Liu, Z., Jiang, Z.: Combining Factorization Model and Additive Forest for Collaborative Followee Recommendation, KDD CUP (2012)
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: International Conf. on Machine Learning (2010)
Chen, Y., Yan, T.W.: Position-normalized click prediction in search advertising. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 795–803. ACM (2012)
Wang, T., Bian, J., Liu, S., Zhang, Y., Liu, T.-Y.: Psychological advertising: exploring user psychology for click prediction in sponsored search. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 563–571. ACM (2013)
Kanagal, B., Ahmed, A., Pandey, S., Josifovski, V., Garcia-Pueyo, L., Yuan, J.: Focused matrix factorization for audience selection in display advertising. In: Data Engineering (ICDE), pp. 386–397 (April 2013)
Wu, J.: Collaborative Filtering On the Netix Prize Dataset, Peking University doctoral dissertation, pp. 87–104 (May 2010)
Lathauwer, L.D., Moor, B.D., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253–1278 (2000)
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Shan, L., Lin, L., Shao, D., Wang, X. (2014). CTR Prediction for DSP with Improved Cube Factorization Model from Historical Bidding Log. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_3
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DOI: https://doi.org/10.1007/978-3-319-12643-2_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12642-5
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