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The Web 2.0 era is characterized by the emergence of a very large amount of live content. A real time and fine grained content filtering approach can precisely keep users up-to-date the information that they are interested. The key of the approach is to offer a scalable match algorithm. One might treat the content match as a special kind of content search, and resort to the classic algorithm [5]....
Geo-textual data are generated in abundance. Recent studies focused on the processing of spatial keyword queries which retrieve objects that match certain keywords within a spatial region. To ensure effective retrieval, various extensions were done including the allowance of errors in keyword matching and
can be reduced by limiting the optimization scope to a relatively small number of most important objects. We quantitatively evaluate our approach on keyword index placement for full-text search engines using real traces of 3.7 million web pages and 6.8 million search queries. Compared to the correlation-oblivious random
With the rapid development of information technology, search engine optimization (SEO) technology has attracted more and more attentions. In order to improve their website visit quantity, SEO techniques can make a better ranking in the search result using the keyword selection and deployment, high quality back links
In this paper, we propose novel techniques to reduce bandwidth cost in a continuous keyword query processing system that is based on a distributed hash table. We argue that query indexing and document announcement are of significant importance towards this goal. Our detailed simulations show that our proposed
When using Information Retrieval (IR) systems, users often present search queries made of ad-hoc keywords. It is then up to the information retrieval systems (IRS) to obtain a precise representation of the user's information need and the context (preferences) of the information. To address this problem, we investigate
various applications. This paper presents a novel approach - Sparse Matrix Sparse Vector Multiplication (SpMSpV) to utilize sparse input vector efficiently. To demonstrate efficiency of the proposed algorithm, it has been applied to keyword based document search, where sparse matrix is used as index structure of text
edges will be built among the blogs which belong to the same result set gotten through the Google blog searching by one keyword. Then the problem of recommender is translated into the clustering of a hyper graph. Our multilevel clustering algorithm is then used to do the segmentation. And we set a new optimization index
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.