@inproceedings{lu2009exploit, abstract = {In this poster, we investigate how to enhance web clustering by leveraging the tripartite network of social tagging systems. We propose a clustering method, called "Tripartite Clustering", which cluster the three types of nodes (resources, users and tags) simultaneously based on the links in the social tagging network. The proposed method is experimented on a real-world social tagging dataset sampled from del.icio.us. We also compare the proposed clustering approach with K-means. All the clustering results are evaluated against a human-maintained web directory. The experimental results show that Tripartite Clustering significantly outperforms the content-based K-means approach and achieves performance close to that of social annotation-based K-means whereas generating much more useful information.}, address = {New York, NY, USA}, author = {Lu, Caimei and Chen, Xin and Park, E. K.}, booktitle = {CIKM '09: Proceeding of the 18th ACM conference on Information and knowledge management}, doi = {10.1145/1645953.1646167}, interhash = {e192e53972f28d78f1ecbffbfea08bed}, intrahash = {a120cece36e15b12321c87e7d0938d73}, isbn = {978-1-60558-512-3}, location = {Hong Kong, China}, pages = {1545--1548}, publisher = {ACM}, title = {Exploit the tripartite network of social tagging for web clustering}, url = {http://portal.acm.org/citation.cfm?id=1646167&dl=GUIDE&coll=GUIDE&CFID=93888742&CFTOKEN=72927742}, year = 2009 } @inproceedings{bekkerman2005multiway, abstract = {We present a novel unsupervised learning scheme that simultaneously clusters variables of several types (e.g., documents, words and authors) based on pairwise interactions between the types, as observed in co-occurrence data. In this scheme, multiple clustering systems are generated aiming at maximizing an objective function that measures multiple pairwise mutual information between cluster variables. To implement this idea, we propose an algorithm that interleaves top-down clustering of some variables and bottom-up clustering of the other variables, with a local optimization correction routine. Focusing on document clustering we present an extensive empirical study of two-way, three-way and four-way applications of our scheme using six real-world datasets including the 20 News-groups (20NG) and the Enron email collection. Our multi-way distributional clustering (MDC) algorithms consistently and significantly outperform previous state-of-the-art information theoretic clustering algorithms.}, address = {New York, NY, USA}, author = {Bekkerman, Ron and El-Yaniv, Ran and McCallum, Andrew}, booktitle = {ICML '05: Proceedings of the 22nd International Conference on Machine learning}, doi = {10.1145/1102351.1102357}, interhash = {25609f84a6916c1664e61d8618f46a32}, intrahash = {2921f89f8663e7bcc122a2a77c66e7c2}, isbn = {1-59593-180-5}, location = {Bonn, Germany}, pages = {41--48}, publisher = {ACM}, title = {Multi-way distributional clustering via pairwise interactions}, url = {http://portal.acm.org/citation.cfm?id=1102351.1102357}, year = 2005 } @inproceedings{popescul01probabilistic, abstract = {Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmann's aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not...}, address = {Seattle, Washington}, author = {Popescul, Alexandrin and Ungar, Lyle and Pennock, David and Lawrence, Steve}, booktitle = {17th Conference on Uncertainty in Artificial Intelligence}, interhash = {429bcf0381d2b7b9ab95eea7d3a65776}, intrahash = {ae7ce7b8d1a31e81f9aa8b8367039506}, month = {August 2--5}, pages = {437--444}, title = {Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments}, url = {http://citeseer.ist.psu.edu/popescul01probabilistic.html}, year = 2001 }