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Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason...
The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users' interests and preferences to make personalized recommendations. To solve the scalability problem of the current user profiling and recommender systems, this paper proposes a parallel user profiling approach and a scalable recommender system. The current advanced cloud...
The recent emergence of location-based social networking services is revolutionizing Web-based social networking allowing users to share real-life experiences via geo-tagged user-generated multimedia content. One of the key challenges of the Web-based social networks as an information sharing and exchanging channel is how to manage healthy relationships among community users and ensure the quality...
Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviors such as purchase behavior, click streams, and browsing history etc., the tagging information implies userpsilas important personal interests and preferences information, which can be used to recommend personalized items to users. This paper...
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.