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Language Model (LM) constitutes one of the key components in Keyword Spotting (KWS). The rapid development of the World Wide Web (WWW) makes it an extremely large and valuable data source for LM training, but it is not optimal to use the raw transcripts from WWW due to the mismatch of content between the web corpus
Inspired by the great success of information retrieval (IR) style keyword search on the Web, keyword search on XML has emerged recently. The difference between text database and XML database results in three new challenges: (1) Identify the user search intention, i.e. identify the XML node types that user wants to
Search engines usually return relevant sorted results based on the keywords. Because of the lack of considering the user's current search interest and intention, this kind of strategy may not meet users' personalized search requirements. In order to retrieve results associated with the user's current search interest
The Web has the potential to become the world's largest knowledge base. In order to unleash this potential, the wealth of information available on the Web needs to be extracted and organized. There is a need for new querying techniques that are simple and yet more expressive than those provided by standard keyword
Using disaggregated data from a Chinese search engine we jointly model ad rank and performance for hospitality related keyword searches. As a result of our modeling framework we can better determine the optimal keyword bidding strategy for an advertiser given the search engine's control over ad rank. Our approach
Domain — specific search focuses on one area of knowledge. Applying broad based ranking algorithms to vertical search domains is not desirable. The broad based ranking model builds upon the data from multiple domains existing on the web. Vertical search engines attempt to use a focused crawler that index only relevant web pages to a predefined topic. With Ranking Adaptation Model, one can adapt an...
profiles five kinds of academic resources from four features including resource type, disciplinary distribution, keyword distribution and LDA topic distribution. After fusing user behaviors and resource profiles, the users' preferences are modeled. Finally, the top-N recommendation is made according to user's interest value
can meet the venereal-disease suspected patients' privacy need. This paper will use search data in the prediction of the incidence of gonorrhea, begin from theory analysis to reveal the relationship between the Baidu search keyword search volume and gonorrhea incidence, and then apply quantitative empirical analysis
With increasing adoption and presence of Web services, designing novel approaches for efficient Web services recommendation has become steadily more important. Existing Web services discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant Web service search engines, which
growth of computing power. Although other analytic approaches also benefit from this trend, keyword searches of several scholarly search engines reveal that the reliance on simulation is increasing more rapidly. A descriptive analysis paints a compelling picture: simulation is frequently a researcher's preferred method for
In a sponsored search market, the problem of measuring the intensity of competition among advertisers is increasingly gaining prominence today. Usually, search providers want to monitor the advertiser communities that share common bidding keywords, so that they can intervene when competition slackens. However, to the
framework, selected keywords, composed the keywords into composite index, found a strong correlation, and finally a result of prediction sales was given in the end of the paper.
model in the data set of flickr. The final ranked pictures are the combination of keywords and users' preference matching. The experiment proves that our method is better than both non-personalization method and common personalization method.
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