Adapting K-nearest neighbor for tag recommendation in Folksonomies
Jonathan F. Gemmell, Thomas Schimoler, Maryam Ramezani, Bamshad Mobasher · 2009
Folksonomies, otherwise known as Collaborative Tagging Systems, enable Internet users to share, annotate and search for online resources with user selected labels called tags. Tag recommendation, the suggestion of an ordered set of tags during the annotation process, reduces the user effort from a keyboard entry to a mouse click. By simplifying the annotation process tagging is promoted, noise in the data is reduced through the elimination of discrepancies that result in redundant tags, and ambiguous tags may be avoided. Tag recommenders can suggest tags that maximize utility, offer tags the user may not have previously considered or steer users toward adopting a core vocabulary. In sum, tag recommendation promotes a denser dataset that is useful in its own right or can be exploited by a myriad of data mining techniques for additional functionality. While there exists a long history of recommendation algorithms, the data structure of a Folksonomy is distinct from those found in traditional recommendation problems. We first explore two data reduction techniques, p-core processing and Hebbian deflation, then demonstrate how to adapt K-Nearest Neighbor for use with Folksonomies by incorporating user, resource and tag information into the algorithm. We further investigate multiple techniques for user modeling required to compute the similarity among users. Additionally we demonstrate that tag boosting, the promoting of tags previously applied by a user to a resource, improves the coverage and accuracy of K-Nearest Neighbor. These techniques are evaluated through extensive experimentation using data collected from two real Collaborative Tagging Web sites. Finally the modified K-Nearest Neighbor algorithm is compared with alternative techniques based on popularity and link analysis. We find that K-Nearest Neighbor modified for use with Folksonomies generates excellent recommendations, scales well with large datasets, and is applicable to both narrow and broadly focused Folksonomies.