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An important and challenging task in any keyword-based search system in text documents or relational databases is the capability of the system to find additional results besides the actual search results and present them to the users as recommendations. This function allows the records that might be of interest to the
Mind maps are used by millions of people. In this paper we present how information retrieval on mind maps could be used to enhance expert search, document summarization, keyword based search engines, document recommender systems and determining word relatedness. For instance, words in a mind map could be used for
As personalization technologies are widely used, preference extraction is becoming important. In this work, we propose a preference extraction method on the basis of applications that are installed on a user's smart device. In this method, keywords are extracted from descriptions of the installed applications on an
their personal file system by leveraging semantic relationships available on the Web. More specifically, JabberWocky is using keyword/resource associations of social bookmarking web sites as a basis for recommending keywords for files. We chose social bookmarking web sites because of their popularity and because the
data. Besides, most of existing recommendation systems present the same static ratings and rankings of items to different users without considering their different needs, and therefore fails to meet users personalized requirements. This paper uses English language Keyword list and domain thesaurus based personalized
in this study. The first is a baseline approach which is based on simple keyword mapping technique. The second approach, Co-Citation Selection (CCS), is based on the collaborative filtering in which neighboring papers is selected and weighted into publication citation prediction. To compare between two approaches, we
Personalized information retrieval and recommendation systems have been proposed to deliver the right information to users with different interests. However, most of previous systems are using keyword frequencies as the main factor for personalization, and as a result, they could not analyze semantic relations between
Many e-commerce web sites such as online book retailers or specialized information hubs such as online movie databases make use of recommendation systems where users are directed to items of interests based on past user interactions. While keyword based approaches are naive and do not take content or context into
In this paper is presented a method for automated generation of medical recommendations, using the combined power of the topic maps and expert systems. To obtain new knowledge from topic maps and to integrate this knowledge with a medical decision making systems, keywords for interrogation are needed. The keywords
selection behaviors. In particular, considering that such influential surrounding context information in microblogs includes keywords related to restaurant assessment, we propose a method for automatically determining the keywords to extract the context information by analyzing online reviews, which have been gathered also
More and more content on the Web is generated by users. To organize this information and make it accessible via current search technology, tagging systems have gained tremendous popularity. Especially for multimedia content they allow to annotate resources with keywords (tags) which opens the door for classic text
With the increase in the number of user reviews on user review sites, useful tools for extracting good and bad points of services so that users can easily and intuitively understand the quality of the services are required. If the annotations are selected from the pre-defined list, there can always be missing keywords
emerged as one successful approach to tackle the problem of information overload. Traditional recommender systems suggest research items using well-known text mining techniques, however they fail when there are no identical keywords to match searches. In order to overcome this and other limitations, several studies have been
feedback and system log, then set up the social networks. According to the input keywords and types of recommender, more recommendation information can be generated. This model has been implemented as a recommendation module in an academic search system Gloss, deployed at the WSI Laboratory of Graduate University of Chinese
as titles, abstracts, keywords and the Chinese Library Classification Codes (CLCCs). According to the reviewer's interest model, we then propose a recommendation approach, which can send a paper published online to the reviewers that are experts in the scoop of the paper. Experimental results show that our
Social bookmarking and other Web sites allow users submitting their resources and labeling them with arbitrary keywords, called tags, to create folksonomies. These sites usually provide their users tag recommendations in order to help them to find relevant information and resources. However, only very basic techniques
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