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.
There are also extensive research about keywords suggestion. In @cite_0 the keywords are suggested based on concept hierarchy mapping therefore the suggestions are not limited to the bag-of-words of the webpage, and may expand to non-obvious ones which are categorized in bigger concepts. The work of @cite_15 @cite_10 recognise that the bid prices for hot keywords are high therefore would cost more, and try to find related non-obvious keywords that are cheaper. Although these keywords may have lower traffic, but when combined the traffic could match that of a hot one, while these keywords cost still less.
@cite_0 @cite_15 @cite_10
2045729350 2962984063 2154463588 1990387666
The increasing growth of the World Wide Web constantly enlarges the revenue generated by search engine advertising. Advertisers bid on keywords associated with their products to display their ads on the search result pages. Keyword suggestion methods are proposed to fill the gap between the keywords chosen by advertisers and the popular queries, through finding new relevant keywords according to some statistical information (for example, the keyword co-occurrence). However, there is little effort taking semantic information, such as concept hierarchy, into account. In this paper, we propose a novel keyword suggestion method that fully exploits the semantic knowledge among concept hierarchy. Given a keyword, we first match it with some relevant concepts. Then the relevant concepts are used with their hierarchy to fertilize the meanings of the keywords. Finally new keywords are suggested according to the concept information rather than the statistical co-occurrence of the keyword itself. Experimental results show that our proposed method can successfully provide suggestion that meets the accuracy and coverage requirements Abstract Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (, 2013; , 2014; , 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way. Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (, 2014; , 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10 F -score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https: github.com lluisgomez TextProposals . Keyword3 generation for search engine advertising is an important problem for sponsored search or paidplacement advertising. A recent strategy in this area is bidding on nonobvious yet relevant words, which are economically more viable. Targeting many such nonobvious words lowers the advertising cost, while delivering the same click volume as expensive words. Generating the right nonobvious yet relevant keywords is a challenging task. The challenge lies in not only finding relevant words, but also in finding many such words. In this paper, we present TermsNet, a novel approach to this problem. This approach leverages search engines to determine relevance between terms and captures their semantic relationships as a directed graph. By observing the neighbors of a term in such a graph, we generate the common as well as the nonobvious keywords related to a term. This paper proposes an effective term suggestion approach to interactive Web search. Conventional approaches to making term suggestions involve extracting co-occurring keyterms from highly ranked retrieved documents. Such approaches must deal with term extraction difficulties and interference from irrelevant documents, and, more importantly, have difficulty extracting terms that are conceptually related but do not frequently co-occur in documents. In this paper, we present a new, effective log-based approach to relevant term extraction and term suggestion. Using this approach, the relevant terms suggested for a user query are those that co-occur in similar query sessions from search engine logs, rather than in the retrieved documents. In addition, the suggested terms in each interactive search step can be organized according to its relevance to the entire query session, rather than to the most recent single query as in conventional approaches. The proposed approach was tested using a proxy server log containing about two million query transactions submitted to search engines in Taiwan. The obtained experimental results show that the proposed approach can provide organized and highly relevant terms, and can exploit the contextual information in a user's query session to make more effective suggestions.