| dc.description.abstract | In recent years, web advertising has become one of the most commonly-used marketing channels. Sponsored search and contextual advertising are the two main categories of text-based web advertising. Our research focuses on the contextual advertising. As indicated in the literature, the contextual advertising suffers from the four major problems, including synonyms, multimedia content, limited amount of text in advertisement, and display characteristics. In this work, our aim is to propose a new contextual advertising method to match ads and web pages. The advantages of our proposed method are that it can avoid the problems caused by multimedia content and limited amount of text problems.
In the literature, the methods to match ads and web pages can be categorized into two main categories, which are vector space model and keyword based model. Both approaches have their own different weaknesses. For vector space model approach, it is difficult to build vectors for non-text-based ads because these ads include mainly multimedia material and have only very limited text; for keyword based model approach, most information in the web page is not used since we only select few keywords rather than a full vector to represent a web page.
Since advertisers understand their ads well, this work assumes that the multimedia ads would be associated with keyword tags provided by their advertisers, based on which we propose a new approach to recommend ads to web pages. In our approach, we combine the two traditional approaches and represent a web page by a term vector and represent an ad by the keyword tags proposed by its advertiser. Then, a matching mechanism is developed to compute the similarities between the vectors and keywords tags. An experiment and evaluation are carried out to demonstrate the performance of the proposed method. The results show that it performs better than traditional information-retrieval methods.
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