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Web users and content are increasingly being geo-positioned. This development gives prominence to spatial keyword queries, which involve both the locations and textual descriptions of content. We study the efficient processing of continuously moving top-k spatial keyword (MkSK) queries over spatial keyword data. State
The purpose of this paper is to provide a solution of extracting appropriate keywords to identify meaningful learning-contents on the Web. There are some issues in identifying documents that have learning content. Firstly, the documents need to be identified according to the learning area of a student's school year
Internet is becoming an increasingly important platform for ordinary life and work. It is expected that keyword extraction can help people quickly find hot spots on the web, since keywords in a document provide important information about the content of the document. In this paper, we propose to use text clustering
Peer-to-peer approaches bring one perfect alternative for the Web content search. However, how to search and retrieve the data based on the content query is still an open problem for peer-to-peer systems. In this paper we propose History-based Multi-keywords Search(HMS) in unstructured peer-to-peer systems, which only
Text keywords at different semantic levels have different semantic representation abilities. Although words have been organized by semantic dictionaries (e.g. WordNet) with exact semantics, the dictionaries can not be constructed automatically by machine and there are still many words which are not included in the
classic statistical method for sentence alignment, we propose an improved approach to align the initial bilingual resources, in which two factors, bilingual keyword pairs and matching patterns are introduced. Experimental results show that our sentence aligner supported by the new approach achieves performance enhancement by
The speedy evolution in web environment and progression in technology have led us to access and manage tremendous images easily in various areas. Current internet image search engines purely trust on the text based information around the images. Keywords supplied by user can not specify content of images exactly
In this research, we used a proxy server to search for information related to the userpsilas browsed Web pages. From the records of the proxy server we constructed a profile of the userpsilas browsing habits. At the end of the userpsilas search subsystem, we will use content based concept to extract keywords to obtain
the registered bidders. Given (1) the valuation of the advertisers competing for sponsored slots corresponding to a keyword, and (2) relevant click-through rates, the proposed algorithm generates a bid profile that can be input to a standard generalized second price based sponsored search auction mechanism. The bid
The insufficiency of consumer's trust is one of bottlenecks to China online shopping, and now most Web sites resort to the renowned third party or trust displaying mechanism to promoting consumer's trust. However, whether the two kinds of trust promotion strategies above have the same effect? And whether their own attributes and assurance contents have the significant influence on consumer's trust?...
time. Comparing the 'like' query in the standard SQL in relational databases, which can not decide the similarity according users' interests when keywords appear in several different fields, a novel similarity evaluation is given in algorithm of the personalized recommendation. Using the method, a personalized digital
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