%0 Conference Paper %1 citeulike:7353648 %A Chen, Wen Y. %A Zhang, Dong %A Chang, Edward Y. %B Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining %C New York, NY, USA %D 2008 %I ACM %K collaborative-filtering community recommender %P 115--123 %R 10.1145/1401890.1401909 %T Combinational collaborative filtering for personalized community recommendation %U http://dx.doi.org/10.1145/1401890.1401909 %X Rapid growth in the amount of data available on social networking sites has made information retrieval increasingly challenging for users. In this paper, we propose a collaborative filtering method, Combinational Collaborative Filtering (CCF), to perform personalized community recommendations by considering multiple types of co-occurrences in social data at the same time. This filtering method fuses semantic and user information, then applies a hybrid training strategy that combines Gibbs sampling and Expectation-Maximization algorithm. To handle the large-scale dataset, parallel computing is used to speed up the model training. Through an empirical study on the Orkut dataset, we show CCF to be both effective and scalable. %@ 978-1-60558-193-4

@inproceedings{citeulike:7353648, abstract = {{Rapid growth in the amount of data available on social networking sites has made information retrieval increasingly challenging for users. In this paper, we propose a collaborative filtering method, Combinational Collaborative Filtering (CCF), to perform personalized community recommendations by considering multiple types of co-occurrences in social data at the same time. This filtering method fuses semantic and user information, then applies a hybrid training strategy that combines Gibbs sampling and Expectation-Maximization algorithm. To handle the large-scale dataset, parallel computing is used to speed up the model training. Through an empirical study on the Orkut dataset, we show CCF to be both effective and scalable.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Chen, Wen Y. and Zhang, Dong and Chang, Edward Y.}, biburl = {https://www.bibsonomy.org/bibtex/2c2b8ac0ba27775bd1c343ced8b2c94be/aho}, booktitle = {Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {7353648}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1401909}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1401890.1401909}, doi = {10.1145/1401890.1401909}, interhash = {95ffe4cb59407a52633bd22b10f257e5}, intrahash = {c2b8ac0ba27775bd1c343ced8b2c94be}, isbn = {978-1-60558-193-4}, keywords = {collaborative-filtering community recommender}, location = {Las Vegas, Nevada, USA}, pages = {115--123}, posted-at = {2010-06-23 19:19:58}, priority = {4}, publisher = {ACM}, series = {KDD '08}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Combinational collaborative filtering for personalized community recommendation}}, url = {http://dx.doi.org/10.1145/1401890.1401909}, year = 2008 }