Abstract
Contextual advertising is a form of online advertising presenting consistent revenue growth since its inception. In this work, we study the problem of recommending a small set of ads to a user based solely on the currently viewed web page, often referred to as content-targeted advertising. Matching ads with web pages is a challenging task for traditional information retrieval systems due to the brevity and sparsity of advertising text, which leads to the widely recognized vocabulary impedance problem. To this end, we propose the use of lexical graphs created from web corpora as a means of computing improved content similarity metrics between ads and web pages. The results of our experimental study provide evidence of significant improvement in the perceived relevance of the recommended ads.
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References
Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In: Proceedings of the 16th international conference on World Wide Web (WWW 2007), pp. 521–530. ACM, New York (2007)
Ribeiro-Neto, B., Cristo, M., Golgher, B.G., de Moura, E.S.: Impedance Coupling in Content-targeted Advertising. In: Proceedings of the 28th annual international ACM SIGIR conference (SIGIR 2005), pp. 496–503. ACM, New York (2005)
Lacerda, A., Cristo, M., Goncalves, M.A., Fan, W., Ziviani, N., Ribeiro-Neto, B.: Learning to advertise. In: Proceedings of the 29th annual international ACM SIGIR conference (SIGIR 2006), pp. 549–556. ACM, New York (2006)
Murdock, V., Ciaramita, M., Plachouras, V.: A noisy-channel approach to contextual advertising. In: Proceedings of the 1st Workshop on Data Mining and Audience intelligence for Advertising (ADKDD 2007), pp. 21–27. ACM, New York (2007)
Yih, W., Goodman, J., Carvalho, V.R.: Finding advertising keywords on web pages. In: Proceedings of the 15th international conference on World Wide Web (WWW 2006), New York, pp. 213–222 (2006)
Mishne, G., de Rijke, M.: Language model mixtures for contextual ad placement in personal blogs. In: Salakoski, T., Ginter, F., Pyysalo, S., Pahikkala, T. (eds.) FinTAL 2006. LNCS, vol. 4139, pp. 435–446. Springer, Heidelberg (2006)
Broder, A., Fontoura, M., Josifovski, V., Riedel, L.: A semantic approach to contextual advertising. In: Proceedings of the 30th annual international ACM SIGIR conference (SIGIR 2007), pp. 559–566. ACM, New York (2007)
Widdows, D., Dorow, B.: A graph model for unsupervised lexical acquisition. In: Proceedings of the 19th international conference on Computational Linguistics, pp. 1–7. Association for Computational Linguistics, Morristown (2002)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communications of the ACM 18(11), 613–620 (1975)
Cohen, J.: A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1), 37–46 (1960)
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Papadopoulos, S., Menemenis, F., Kompatsiaris, Y., Bratu, B. (2009). Lexical Graphs for Improved Contextual Ad Recommendation. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds) Advances in Information Retrieval. ECIR 2009. Lecture Notes in Computer Science, vol 5478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00958-7_21
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DOI: https://doi.org/10.1007/978-3-642-00958-7_21
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