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by the network -- descriptive keywords, or tags. In this paper we present a model that enables keyword discovery methods through the interpretation of the network as a graph, solely relying on keywords that categorize or describe productive items. The model and keyword discovery methods presented in this paper avoid
In this paper, we propose an expert search scheme in social networks. The proposed scheme updates a profile by analyzing recent activities, and considers the reliability scores of users and users' ratings that are computed by the updated profile. A user's profile is created by extracting a keyword from the recent
In traditional collaborative filtering recommendation, the matrix sparsity and cold start restricted the accuracy of system. In this paper, we develop a way to enhance the recommendation effectiveness by merging neighborhood relationship and users keyword of social network information into collaborative filtering. We
Websites that provide content creation and sharing features have become quite popular recently. These sites allow users to categorize and browse content using "tags” or free-text keyword topics. Since users contribute and tag social media content across a variety of social web platforms, creating new
In order to solve the problem that we can only collect data from one single data source at some fixed time after mining the keywords in a rather superficial level, and to take full use of the information returned by search engines to construct the social relationship network based on the semantic link of the searched
In this study we try to understand how physicians can benefit from a social healthcare forum for a chronic digestive disease like irritable bowel syndrome (IBS). Based on over half a million posts over 16 years, we compared the frequencies of selected keywords and their concurrent and time-lagged concordance with
based social network. We propose an efficient mobile social network to facilitate contents sharing over mobile ad hoc networks, in which social communities are built based on the interesting keywords, location histories and the current locations of different users. The users with common interest keywords, similar location
need to handle the text information gathered from Plurk (the world-famous social network) to carry out regularization. We make use of the data mining method to analyze the information on the subject of music interest. We classify various types of songs. They also substitute these keywords called different degree of
to collect active commercial entities and a commercial relation lexicon is created to collect keywords that flag commercial relations. Illustration and applications are also discussed, which undoubtedly discloses a promising future of commercial network study.
for discussion analysis. It is based on message-based graphs where each vertex represents a message object and each edge points out which message the specific node replies to. The edges can be weighted by the keywords that characterize the exchanged messages. This model allows a content-oriented representation of the
) information content due to occurrence of a property with respect to all the properties in a description base ii) unpredictability of an association due to participation of its properties in multiple domains iii) the extent of match between user specified keywords and properties and iv) the popularity of nodes involved in a
Although the problem of spam detection in email is well understood and has been extensively researched, a significant portion of emails today are spam. A most widely used method to detect spam involves content filtering, where the spam detector scans the received email for keywords. However, the same approach cannot
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