Personalized recommendation in social tagging systems using hierarchical clustering Export

@inproceedings{citeulike:3482157, abstract = {Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.}, address = {New York, NY, USA}, author = {Shepitsen, Andriy and Gemmell, Jonathan and Mobasher, Bamshad and Burke, Robin}, booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems}, citeulike-article-id = {3482157}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1454008.1454048}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1454008.1454048}, doi = {10.1145/1454008.1454048}, isbn = {978-1-60558-093-7}, keywords = {2008, clustering, hierarchical, tagging}, location = {Lausanne, Switzerland}, pages = {259--266}, posted-at = {2008-12-05 06:58:47}, priority = {3}, publisher = {ACM}, title = {Personalized recommendation in social tagging systems using hierarchical clustering}, url = {http://dx.doi.org/10.1145/1454008.1454048}, year = {2008} }

TY - CONF ID - citeulike:3482157 L3 - citeulike-article-id:3482157 N2 - Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies. CY - Lausanne, Switzerland SN - 978-1-60558-093-7 AD - New York, NY, USA EP - 266 TI - Personalized recommendation in social tagging systems using hierarchical clustering PB - ACM T2 - RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems SP - 259 KW - 2008 KW - clustering KW - hierarchical KW - tagging AU - Shepitsen, Andriy AU - Gemmell, Jonathan AU - Mobasher, Bamshad AU - Burke, Robin PY - 2008/// UR - http://dx.doi.org/10.1145/1454008.1454048 ER -