This short paper describes a novel technique for generating personalized
ag recommendations for users of social book- marking sites such
s del.icio.us. Existing techniques recom- mend tags on the basis
f their popularity among the group of all users; on the basis of
ecent use; or on the basis of simple heuristics to extract keywords
rom the url being tagged. Our method is designed to complement these
pproaches, and is based on recommending tags from urls that are
imilar to the one in question, according to two distinct similarity
etrics, whose principal utility covers complementary cases.

@inproceedings{byde2007personalized, author = {Byde, Andrew and Wan, Hui and Cayzer, Steve}, title = {Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics}, booktitle = {Proceedings of the International Conference on Weblogs and Social
edia}, year = {2007}, url = {http://www.icwsm.org/papers/paper47.html}, keywords = {tagging, similarity, recommender, tag, content}, abstract = {This short paper describes a novel technique for generating personalized
ag recommendations for users of social book- marking sites such
s del.icio.us. Existing techniques recom- mend tags on the basis
f their popularity among the group of all users; on the basis of
ecent use; or on the basis of simple heuristics to extract keywords
rom the url being tagged. Our method is designed to complement these
pproaches, and is based on recommending tags from urls that are
imilar to the one in question, according to two distinct similarity
etrics, whose principal utility covers complementary cases.} }

%0 = inproceedings %A = Byde, Andrew and Wan, Hui and Cayzer, Steve %B = Proceedings of the International Conference on Weblogs and Social
edia %D = 2007 %T = Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics %U = http://www.icwsm.org/papers/paper47.html

This short paper describes a novel technique for generating personalized tag recommendations for users of social book- marking sites such as del.icio.us. Existing techniques recom- mend tags on the basis of their popularity among the group of all users; on the basis of recent use; or on the basis of simple heuristics to extract keywords from the url being tagged. Our method is designed to complement these approaches, and is based on recommending tags from urls that are similar to the one in question, according to two distinct similarity metrics, whose principal utility covers complementary cases.

@inproceedings{Byde2007, author = {Byde, Andrew and Wan, Hui and Cayzer, Steve}, title = {Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics}, booktitle = {Proceedings of the International Conference on Weblogs and Social Media}, year = {2007}, url = {http://www.icwsm.org/papers/paper47.html}, keywords = {tagging, taggingsurvey, recommender, collaborative, social, filtering, bookmarking, toread}, abstract = {This short paper describes a novel technique for generating personalized tag recommendations for users of social book- marking sites such as del.icio.us. Existing techniques recom- mend tags on the basis of their popularity among the group of all users; on the basis of recent use; or on the basis of simple heuristics to extract keywords from the url being tagged. Our method is designed to complement these approaches, and is based on recommending tags from urls that are similar to the one in question, according to two distinct similarity metrics, whose principal utility covers complementary cases.} }

%0 = inproceedings %A = Byde, Andrew and Wan, Hui and Cayzer, Steve %B = Proceedings of the International Conference on Weblogs and Social Media %D = 2007 %T = Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics %U = http://www.icwsm.org/papers/paper47.html

@inproceedings{michlmayr07, author = {Michlmayr, Elke and Cayzer, Steve}, title = {Learning User Profiles from Tagging Data and Leveraging them for Personal(ized) Information Access}, booktitle = {Tagging and Metadata for Social Information Organization Workshop, WWW07}, year = {2007}, keywords = {tagging, taggingsurvey, profile, folksonomy, user, toread} }

%0 = inproceedings %A = Michlmayr, Elke and Cayzer, Steve %B = Tagging and Metadata for Social Information Organization Workshop, WWW07 %D = 2007 %T = Learning User Profiles from Tagging Data and Leveraging them for Personal(ized) Information Access