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International Journal of Intelligent Systems; Volume 9999, Issue 9999 - Wiley InterScience
Recommender Systems for Social Bookmarking
In this thesis, we investigate how recommender systems can be applied to the domain of social bookmarking. More specifically, we want to investigate the task of item recommendation. For this purpose, interesting and relevant items---bookmarks or scientific articles---are retrieved and recommended to the user. Recommendations can be based on a variety of information sources about the user and the items. It is a difficult task as we are trying to predict which items out of a very large pool would be relevant given a user's interests, as represented by the items which the user has added in the past. In our experiments we distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. We are among the first to investigate this content-based aspect of recommendation for social bookmarking websites. We compare and combine the content-based aspect with the more common usage-based approaches.
CEUR-WS.org/Vol-532 - Recommender Systems and the Social Web 2009
Proceedings of the Workshop on Recommender Systems and the Social Web,
collocated with the 3rd ACM Conference on Recommender Systems (RecSys'09),
SPEAR Algorithm - Michael G. Noll
Telling Experts from Spammers: Expertise Ranking in Folksonomies"
Information Overload, 140 Characters at a Time
Twitter feeds range from truly useful to banal and currently represent one of the fastest growing social networking technologies. EIC Fred Douglis looks at whether Twitter's success will continue, or whether it will ultimately be overtaken by a better, more selective technology
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