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
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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 In this paper the adequacy of the co-word method for mapping the structure of scientific inquiry is explored. Co-word analysis of both the keywords and the titles of a set of papers in acidification research' is undertaken and the results are found to be comparable, though the keyword-derived results provide greater detail. This strongly suggests that keyword indexing doest not, as has sometimes been claimed, distort coword findings. It also points to differences between titles (which often emphasize the supposed originality of an article) and keywords (which tend to show the relationship between the paper and other publications). The paper also explores important differences between the methodological assumptions that underlie the Paris Keele co-word clustering algorithms and the factor analysis method for creating clusters. User reviews of mobile apps often contain complaints or suggestions which are valuable for app developers to improve user experience and satisfaction. However, due to the large volume and noisy-nature of those reviews, manually analyzing them for useful opinions is inherently challenging. To address this problem, we propose MARK, a keyword-based framework for semi-automated review analysis. MARK allows an analyst describing his interests in one or some mobile apps by a set of keywords. It then finds and lists the reviews most relevant to those keywords for further analysis. It can also draw the trends over time of those keywords and detect their sudden changes, which might indicate the occurrences of serious issues. To help analysts describe their interests more effectively, MARK can automatically extract keywords from raw reviews and rank them by their associations with negative reviews. In addition, based on a vector-based semantic representation of keywords, MARK can divide a large set of keywords into more cohesive subsets, or suggest keywords similar to the selected ones.