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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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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