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effective in terms of better precision. Proposed method makes use of keyword clusters for query expansion. Visual features are used for detecting duplicate images in proposed method. Removing duplicates leads to further improve in precision and recall in retrieval result
In many cases keywords from a restricted set of possible keywords have to be assigned to texts. A common way to find the best keywords is to rank terms occurring in the text according to their tf.idf value. This requires a corpus of texts from which document frequencies can be derived. In this paper we show that we
We examine whether aggregate daily Twitter keyword volumes over eight months from November 2011 to June 2012 can be used to predict aggregate daily consumer spending as reported by Gallup. We also examine whether Twitter keyword volume improves predictive ability over prediction based solely on current spending
Cipher text search ability is a promising method to securely store and retrieve outsourced data, like in secure cloud storage. But it is still hard to do fuzzy keyword search over outsourced cipher texts. In INFOCOM 2010, Li et al. [1] proposed a fuzzy keyword search scheme over encrypted data based on edit distance
Query-recommendation systems based on inputted queries have become widespread. These services are effective if users cannot input relevant queries. However, the conventional systems do not take into consideration the relevance between recommended queries. This paper proposes a method of obtaining related queries and clustering them by using the history of query frequencies in query logs. We define...
In this paper we focus on personalized recommendation algorithm for coupon deals, which are very different from deals of other retailers. We first analyzed some sample deals from Groupon and found that deals under category dining, Wellness and activities have a high probability of having the same keywords in the deal
technique called WebPagePrev through context and content keywords. Underlying techniques for context and content memories’ acquisition, storage, decay, and utilization for page re-finding are discussed. A relevance feedback mechanism is also involved to tailor to individual's memory strength and
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
articles are then analysed, and a set of keywords per Wikipedia category are extracted using a modified tf-idf (term frequency-inverse document frequency) model proposed in this paper. To classify a given input document, tf-idf weights are used to extract relevant keywords from the document, which are then matched to the
Search engines are one of the most powerful tools in the Web world today for data retrieval and exploration. Most search engines identify the key word in the sentence or phrase or list of words given by the user and starts mining the Web for the occurrence of keyword in the Web pages. Quite often searching for the key
this problem by automatically dividing the social network of a Twitter user into personal cliques, and annotating each clique with keywords to identify the common ground of a clique. Our proposed clique annotation method extracts keywords from the tweet history of the clique members and individually weights the extracted
monthly automobile sales using sentiment and topical keyword frequencies related to the target brand over time on social media. Our predictive model illustrates how different time scale-based predictors derived from sentiment and topical keyword frequencies can improve the prediction of the future sales.
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
In recent years, user generated content services have become popular. The authors are interested in online novel services. Classification of online novels is difficult because keywords and genre are assigned by the author of the novel without control. In order to overcome the problem faced when category classifying
FCA, a session interest concept is defined as a pair of extent and intent where the extent covers a set of documents selected by the user among the search results and the intent covers a set of keyword features extracted from the selected documents. And, in order to make a concept network grow, we need to calculate the
Remote Electronic Document (CReED) provided an access control to all documents that will grant different privileges to each user of the system. It also utilized a keyword analyzer and result matcher that will make searching and retrieving of documents faster and easier. CReED used a scanner device and file importing tool to
The existing search engines are always lack of the consideration of personalization and display the same search results for different users despite their differences in interesting and purpose. So through analyzing the dynamic search behavior of users, the paper introduces a new method of using a keyword query graph
This paper proposes a system for finding a userpsilas interests on the Internet. It is based on his browsing behaviors and the contents of his visited pages. The system has two features. One is building userpsilas browsing interests implicitly, multiple keyword vectors, one per interest. The other is that it can
information. Keyword based information retrieval technique helps in improving recall of user query result, but having low precision. To improve precision, we adopt semantic information retrieval technique. We are proposing architecture for semantic based information retrieval, in which plain text is read semantically and the
to keyword searching. Thus far, the identification of the facets was either a manual procedure, or relied on apriori knowledge of the facets that can potentially appear in the underlying collection. In this paper, we present an unsupervised technique for automatic extraction of facets useful for browsing text databases
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