As organizations, both business and research-development continue to search better ways to exploit knowledge capital accumulated on the diversified Web; it fosters the need of collaboration among people with similar interest & expertise. In this paper we focus on the problem of discovering people who have particular interests or expertise. The standard approach is to build interest group lists from explicitly registered data. However, doing so assumes one knows what lists should be built, and who ought to be included in each list. We present an alternative approach, which can support a finer grained and dynamically adaptive notion of shared interests. Our approach deduces shared interest relationships between people based on interest similarity calculated by the means of entries written on their blog. Using this approach, a user could search for people by requesting a list of people whose interests are similar to several people known to have the interest in question.
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- An Algorithmic Framework for Collaborative Interest Group Construction
- Springer Berlin Heidelberg
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