%0 Conference Paper %1 citeulike:349900 %A Jeh, Glen %A Widom, Jennifer %B KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining %C New York, NY, USA %D 2002 %I ACM Press %K clustering context, information-retrieval link-mining measure, ranking, similarity, text-mining %P 538--543 %R 10.1145/775047.775126 %T SimRank: a measure of structural-context similarity %U http://dx.doi.org/10.1145/775047.775126

@inproceedings{citeulike:349900, added-at = {2008-02-10T02:19:38.000+0100}, address = {New York, NY, USA}, author = {Jeh, Glen and Widom, Jennifer}, biburl = {https://www.bibsonomy.org/bibtex/29b901eee9786a06b6ef7203e7a920608/brightbyte}, booktitle = {KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {2162785}, comment = {Data: WWW, Scientific Papers, Recommender Systems Task: find similar nodes / similarity measure for nodes Method: SimRank is based on Google's PageRank. They convert the weighted graph structure G to another weighted graph G2, where each v2 = (v,v) and a link e2 = (v2, v2) = ((v', v''), (v''', v'''')) exists if G contains the two edges (v', v''') and (v'', v''''). This SimRank measure can be applied to homogenous (e.g. papers) and bipartite (e.g. user-item) domains Motto: Two objects are similar if they relate to similar objects.}, description = {stuff from citeyoulike}, doi = {10.1145/775047.775126}, interhash = {a6d4531690305dc44937118df813b4b5}, intrahash = {9b901eee9786a06b6ef7203e7a920608}, keywords = {clustering context, information-retrieval link-mining measure, ranking, similarity, text-mining}, pages = {538--543}, priority = {2}, publisher = {ACM Press}, timestamp = {2009-01-23T09:58:50.000+0100}, title = {SimRank: a measure of structural-context similarity}, url = {http://dx.doi.org/10.1145/775047.775126}, year = 2002 }