Domain name registrars and URL shortener service providers place advertisements on the parked domains (Internet domain names which are not in service) in order to generate profits. As the web contents have been removed, it is critical to make sure the displayed ads are directly related to the intents of the visitors who have been directed to the parked domains. Because of the missing contents in these domains, it is non-trivial to generate the keywords to describe the previous contents and therefore the users intents. In this paper we discuss the adaptive keywords extraction problem and introduce an algorithm based on the BM25F term weighting and linear multi-armed bandits. We built a prototype over a production domain registration system and evaluated it using crowdsourcing in multiple iterations. The prototype is compared with other popular methods and is shown to be more effective.
The work of @cite_22 proposed a classifier that uses multiple text features, including how often the term occurs in search query logs, to extract keywords for ads targeting based on logistic regression. The system discussed in @cite_29 first generates candidates by several methods including a translation model capable of generating phrases not appearing in the text of the pages. Then candidates are ranked in a probabilistic framework using both the translation model favouring relevant phrases, as well as a language model favouring well-formed phrases. Another relevant work can be found in @cite_27 . The authors proposed an exploration-exploitation algorithm of sorting keywords in an descending order of profit-to-cost ratio and adaptively identify the set of keywords to bid on based on historical performance, with a daily budget constraint. In this paper we try to find the keywords from the given webpage with additional web knowledge, rather than find profitable ones from a very large set (like 50k). Then we leave the matching between keywords and ads to display networks exchanges, such as Google AdSense.
@cite_27 @cite_29 @cite_22
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Increases in online search activities have spurred the growth of search-based advertising services offered by search engines. These services enable companies to promote their products to consumers based on their search queries. In most search-based advertising services, a company sets a daily budget, selects a set of keywords, determines a bid price for each keyword, and designates an ad associated with each selected keyword. When a consumer searches for one of the selected keywords, search engines then display the ads associated with the highest bids for that keyword on the search result page. A company whose ad is displayed pays the search engine only when the consumer clicks on the ad. If the company's spending has exceeded its daily budget, however, its ads will not be displayed. With millions of available keywords and a highly uncertain clickthru rate associated with the ad for each keyword, identifying the most profitable set of keywords given the daily budget constraint becomes challenging for companies wishing to promote their goods and services via search-based advertising. Motivated by these challenges, we formulate a model of keyword selection in search-based advertising services. We develop an algorithm that adaptively identifies the set of keywords to bid on based on historical performance. The algorithm prioritizes keywords based on a prefix ordering-- sorting of keywords in a descending order of profit-tocost ratio (or "bang-per-buck"). We show that the average expected profit generated by the algorithm converges to near-optimal profits. Furthermore, the convergence rate is independent of the number of keywords and scales gracefully with the problem's parameters. Extensi One of the most prevalent online advertising methods is textual advertising. To produce a textual ad, an advertiser must craft a short creative (the text of the ad) linking to a landing page, which describes the product or service being promoted. Furthermore, the advertiser must associate the creative to a set of manually chosen bid phrases representing those Web search queries that should trigger the ad. For efficiency, given a landing page, the bid phrases are often chosen first, and then for each bid phrase the creative is produced using a template. Nevertheless, an ad campaign (e.g., for a large retailer) might involve thousands of landing pages and tens or hundreds of thousands of bid phrases, hence the entire process is very laborious. Our study aims towards the automatic construction of online ad campaigns: given a landing page, we propose several algorithmic methods to generate bid phrases suitable for the given input. Such phrases must be both relevant (that is, reflect the content of the page) and well-formed (that is, likely to be used as queries to a Web search engine). To this end, we use a two phase approach. First, candidate bid phrases are generated by a number of methods, including a (mono-lingual) translation model capable of generating phrases contained within the text of the input as well as previously "unseen" phrases. Second, the candidates are ranked in a probabilistic framework using both the translation model, which favors relevant phrases, as well as a bid phrase language model, which favors well-formed phrases. Empirical evaluation based on a real-life corpus of advertiser-created landing pages and associated bid phrases confirms the value of our approach, wh A large and growing number of web pages display contextual advertising based on keywords automatically extracted from the text of the page, and this is a substantial source of revenue supporting the web today. Despite the importance of this area, little formal, published research exists. We describe a system that learns how to extract keywords from web pages for advertisement targeting. The system uses a number of features, such as term frequency of each potential keyword, inverse document frequency, presence in meta-data, and how often the term occurs in search query logs. The system is trained with a set of example pages that have been hand-labeled with "relevant" keywords. Based on this training, it can then extract new keywords from previously unseen pages. Accuracy is substantially better than several baseline systems.