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.
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