Personalized tag recommendation using graph-based ranking on multi-type interrelated objects Export

@inproceedings{citeulike:5287244, abstract = {Social tagging is becoming increasingly popular in many Web 2.0 applications where users can annotate resources (e.g. Web pages) with arbitrary keywords (i.e. tags). A tag recommendation module can assist users in tagging process by suggesting relevant tags to them. It can also be directly used to expand the set of tags annotating a resource. The benefits are twofold: improving user experience and enriching the index of resources. However, the former one is not emphasized in previous studies, though a lot of work has reported that different users may describe the same concept in different ways. We address the problem of personalized tag recommendation for text documents. In particular, we model personalized tag recommendation as a "query and ranking" problem and propose a novel graph-based ranking algorithm for interrelated multi-type objects. When a user issues a tagging request, both the document and the user are treated as a part of the query. Tags are then ranked by our graph-based ranking algorithm which takes into consideration both relevance to the document and preference of the user. Finally, the top ranked tags are presented to the user as suggestions. Experiments on a large-scale tagging data set collected from Del.icio.us have demonstrated that our proposed algorithm significantly outperforms algorithms which fail to consider the diversity of different users' interests.}, address = {New York, NY, USA}, author = {Guan, Ziyu and Bu, Jiajun and Mei, Qiaozhu and Chen, Chun and Wang, Can}, booktitle = {SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval}, citeulike-article-id = {5287244}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1571941.1572034}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1571941.1572034}, doi = {10.1145/1571941.1572034}, isbn = {978-1-60558-483-6}, keywords = {graph, tag, tag-suggestion}, location = {Boston, MA, USA}, pages = {540--547}, posted-at = {2009-07-28 11:17:52}, priority = {2}, publisher = {ACM}, title = {Personalized tag recommendation using graph-based ranking on multi-type interrelated objects}, url = {http://dx.doi.org/10.1145/1571941.1572034}, year = {2009} }

TY - CONF ID - citeulike:5287244 L3 - citeulike-article-id:5287244 N2 - Social tagging is becoming increasingly popular in many Web 2.0 applications where users can annotate resources (e.g. Web pages) with arbitrary keywords (i.e. tags). A tag recommendation module can assist users in tagging process by suggesting relevant tags to them. It can also be directly used to expand the set of tags annotating a resource. The benefits are twofold: improving user experience and enriching the index of resources. However, the former one is not emphasized in previous studies, though a lot of work has reported that different users may describe the same concept in different ways. We address the problem of personalized tag recommendation for text documents. In particular, we model personalized tag recommendation as a "query and ranking" problem and propose a novel graph-based ranking algorithm for interrelated multi-type objects. When a user issues a tagging request, both the document and the user are treated as a part of the query. Tags are then ranked by our graph-based ranking algorithm which takes into consideration both relevance to the document and preference of the user. Finally, the top ranked tags are presented to the user as suggestions. Experiments on a large-scale tagging data set collected from Del.icio.us have demonstrated that our proposed algorithm significantly outperforms algorithms which fail to consider the diversity of different users' interests. CY - Boston, MA, USA SN - 978-1-60558-483-6 AD - New York, NY, USA EP - 547 TI - Personalized tag recommendation using graph-based ranking on multi-type interrelated objects PB - ACM T2 - SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval SP - 540 KW - graph KW - tag KW - tag-suggestion AU - Guan, Ziyu AU - Bu, Jiajun AU - Mei, Qiaozhu AU - Chen, Chun AU - Wang, Can PY - 2009/// UR - http://dx.doi.org/10.1145/1571941.1572034 ER -