@inproceedings{ls_leimeister, author = {Blohm, I. and Ott, F. and Bretschneider, U. and Huber, M. and Rieger, M. and Glatz, F. and Koch, M. and Leimeister, J. M. and Krcmar, H.}, title = {Extending Open Innovation Platforms into the real world -
ing Large Displays in Public Spaces}, booktitle = {10. European Academy of Management Conference (EURAM) 2010}, address = {Rome, Italy}, year = {2010}, number = {10}, note = {197 (45-10) }, url = {http://www.uni-kassel.de/fb7/ibwl/leimeister/pub/JML_197.pdf}, keywords = {IdeaMirror, collaborative, community, computing, evaluation, filtering, idea, innovation, itegpub, myown, open, pub_jml, pub_ubr, support, ubiquitous} }

%0 = inproceedings %A = Blohm, I. and Ott, F. and Bretschneider, U. and Huber, M. and Rieger, M. and Glatz, F. and Koch, M. and Leimeister, J. M. and Krcmar, H. %B = 10. European Academy of Management Conference (EURAM) 2010 %C = Rome, Italy %D = 2010 %T = Extending Open Innovation Platforms into the real world -
ing Large Displays in Public Spaces %U = http://www.uni-kassel.de/fb7/ibwl/leimeister/pub/JML_197.pdf

@inproceedings{ls_leimeister, author = {Blohm, I. and Ott, F. and Bretschneider, U. and Huber, M. and Rieger, M. and Glatz, F. and Koch, M. and Leimeister, J. M. and Krcmar, H.}, title = {Extending Open Innovation Platforms into the real world - Using Large Displays in Public Spaces}, booktitle = {10. European Academy of Management Conference (EURAM) 2010}, address = {Rome, Italy}, year = {2010}, number = {10}, note = {197 (45-10) }, url = {http://pubs.wi-kassel.de/wp-content/uploads/2013/03/JML_248.pdf}, keywords = {IdeaMirror, collaborative, community, computing, dempub, evaluation, filtering, idea, innovation, itegpub, open, pub_jml, pub_ubr, support, ubiquitous} }

%0 = inproceedings %A = Blohm, I. and Ott, F. and Bretschneider, U. and Huber, M. and Rieger, M. and Glatz, F. and Koch, M. and Leimeister, J. M. and Krcmar, H. %B = 10. European Academy of Management Conference (EURAM) 2010 %C = Rome, Italy %D = 2010 %T = Extending Open Innovation Platforms into the real world - Using Large Displays in Public Spaces %U = http://pubs.wi-kassel.de/wp-content/uploads/2013/03/JML_248.pdf

Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system.

@inproceedings{parra2009evaluation, author = {Parra, Denis and Brusilovsky, Peter}, title = {Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike}, booktitle = {Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web}, series = {CEUR Workshop Proceedings}, year = {2009}, volume = {467}, url = {http://ceur-ws.org/Vol-467/paper5.pdf}, keywords = {algorithms, citedBy:doerfel2012leveraging, collaborative, evaluation, filtering}, abstract = {Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system. } }

%0 = inproceedings %A = Parra, Denis and Brusilovsky, Peter %B = Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web %D = 2009 %T = Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike %U = http://ceur-ws.org/Vol-467/paper5.pdf

@article{herlocker2004ecf, author = {Herlocker, J.L. and Konstan, J.A. and Terveen, L.G. and Riedl, J.T.}, title = {Evaluating collaborative filtering recommender systems}, journal = {ACM Transactions on Information Systems}, year = {2004}, volume = {22}, number = {1}, pages = {5--53}, keywords = {collaborative, evaluation, filtering, recommender} }

%0 = article %A = Herlocker, J.L. and Konstan, J.A. and Terveen, L.G. and Riedl, J.T. %D = 2004 %T = Evaluating collaborative filtering recommender systems

Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.

@article{Herlocker:2004:ECF:963770.963772, author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Terveen, Loren G. and Riedl, John T.}, title = {Evaluating collaborative filtering recommender systems}, journal = {ACM Trans. Inf. Syst.}, publisher = {ACM}, address = {New York, NY, USA}, year = {2004}, volume = {22}, pages = {5--53}, url = {http://doi.acm.org/10.1145/963770.963772}, doi = {10.1145/963770.963772}, issn = {1046-8188}, keywords = {collaEvaluating, collaborative, eval, evaluation, filtering, recommend, recommendation, recommender, systems}, abstract = {Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.} }

%0 = article %A = Herlocker, Jonathan L. and Konstan, Joseph A. and Terveen, Loren G. and Riedl, John T. %C = New York, NY, USA %D = 2004 %I = ACM %T = Evaluating collaborative filtering recommender systems %U = http://doi.acm.org/10.1145/963770.963772

Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.

@article{herlocker2004evaluating, author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Terveen, Loren G. and Riedl, John T.}, title = {Evaluating collaborative filtering recommender systems}, journal = {ACM Trans. Inf. Syst.}, publisher = {ACM Press}, address = {New York, NY, USA}, year = {2004}, volume = {22}, number = {1}, pages = {5--53}, url = {http://portal.acm.org/citation.cfm?id=963770.963772}, doi = {http://doi.acm.org/10.1145/963770.963772}, issn = {1046-8188}, keywords = {collaborative, evaluation, filtering, recommender}, abstract = {Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.} }

%0 = article %A = Herlocker, Jonathan L. and Konstan, Joseph A. and Terveen, Loren G. and Riedl, John T. %C = New York, NY, USA %D = 2004 %I = ACM Press %T = Evaluating collaborative filtering recommender systems %U = http://portal.acm.org/citation.cfm?id=963770.963772