Exploring social annotations for information retrieval Export

@inproceedings{Zhou2008social-ir, abstract = {Social annotation has gained increasing popularity in many Web-based applications, leading to an emerging research area in text analysis and information retrieval. This paper is concerned with developing probabilistic models and computational algorithms for social annotations. We propose a unified framework to combine the modeling of social annotations with the language modeling-based methods for information retrieval. The proposed approach consists of two steps: (1) discovering topics in the contents and annotations of documents while categorizing the users by domains; and (2) enhancing document and query language models by incorporating user domain interests as well as topical background models. In particular, we propose a new general generative model for social annotations, which is then simplified to a computationally tractable hierarchical Bayesian network. Then we apply smoothing techniques in a risk minimization framework to incorporate the topical information to language models. Experiments are carried out on a real-world annotation data set sampled from del.icio.us. Our results demonstrate significant improvements over the traditional approaches.}, address = {New York, NY, USA}, author = {Zhou, Ding and Bian, Jiang and Zheng, Shuyi and Zha, Hongyuan and Giles, C. Lee}, booktitle = {WWW '08: Proceeding of the 17th international conference on World Wide Web}, citeulike-article-id = {2775088}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1367497.1367594}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1367497.1367594}, doi = {10.1145/1367497.1367594}, isbn = {978-1-60558-085-2}, keywords = {annotation, information, retrieval, social}, location = {Beijing, China}, pages = {715--724}, posted-at = {2009-09-28 07:15:47}, priority = {2}, publisher = {ACM}, title = {Exploring social annotations for information retrieval}, url = {http://dx.doi.org/10.1145/1367497.1367594}, year = {2008} }

TY - CONF ID - Zhou2008social-ir L3 - citeulike-article-id:2775088 N2 - Social annotation has gained increasing popularity in many Web-based applications, leading to an emerging research area in text analysis and information retrieval. This paper is concerned with developing probabilistic models and computational algorithms for social annotations. We propose a unified framework to combine the modeling of social annotations with the language modeling-based methods for information retrieval. The proposed approach consists of two steps: (1) discovering topics in the contents and annotations of documents while categorizing the users by domains; and (2) enhancing document and query language models by incorporating user domain interests as well as topical background models. In particular, we propose a new general generative model for social annotations, which is then simplified to a computationally tractable hierarchical Bayesian network. Then we apply smoothing techniques in a risk minimization framework to incorporate the topical information to language models. Experiments are carried out on a real-world annotation data set sampled from del.icio.us. Our results demonstrate significant improvements over the traditional approaches. CY - Beijing, China SN - 978-1-60558-085-2 AD - New York, NY, USA EP - 724 TI - Exploring social annotations for information retrieval PB - ACM T2 - WWW '08: Proceeding of the 17th international conference on World Wide Web SP - 715 KW - annotation KW - information KW - retrieval KW - social AU - Zhou, Ding AU - Bian, Jiang AU - Zheng, Shuyi AU - Zha, Hongyuan AU - Giles, Lee PY - 2008/// UR - http://dx.doi.org/10.1145/1367497.1367594 ER -