Tom Crecelius
Sebastian Michel
Ralf Schenkel
ABSTRACT
Online communities have become popular for publishing and searching content, and also for connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. Items can be annotated and rated by different users, and users can connect to others that are usually friends and/or share common interests.
We demonstrate a social recommendation system that takes advantages of users connections and tagging behavior to compute recommendations of items in such communities. The advantages can be verified via comparison to a standard IR technique.
- J. L. Herlocker et al. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 2004. Google Scholar
Digital Library
Index Terms
- Social recommendations at work
Recommendations
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