@inproceedings{sarwar2001itembased, acmid = {372071}, address = {New York, NY, USA}, author = {Sarwar, Badrul and Karypis, George and Konstan, Joseph and Riedl, John}, booktitle = {Proceedings of the 10th international conference on World Wide Web}, doi = {10.1145/371920.372071}, interhash = {043d1aaba0f0b8c01d84edd517abedaf}, intrahash = {16f38785d7829500ed41c610a5eff9a2}, isbn = {1-58113-348-0}, location = {Hong Kong, Hong Kong}, numpages = {11}, pages = {285--295}, publisher = {ACM}, title = {Item-based collaborative filtering recommendation algorithms}, url = {http://doi.acm.org/10.1145/371920.372071}, year = 2001 } @article{konstan2012recommender, abstract = {Since their introduction in the early 1990’s, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.}, author = {Konstan, JosephA. and Riedl, John}, doi = {10.1007/s11257-011-9112-x}, interhash = {4bacbfddd599dc935450572bb03df2dc}, intrahash = {f0dbad7662753cd4e0f162fbd7e7a8ca}, issn = {0924-1868}, journal = {User Modeling and User-Adapted Interaction}, language = {English}, number = {1-2}, pages = {101-123}, publisher = {Springer Netherlands}, title = {Recommender systems: from algorithms to user experience}, url = {http://dx.doi.org/10.1007/s11257-011-9112-x}, volume = 22, year = 2012 } @inproceedings{agrahri2008people, abstract = {Search engines are among the most-used resources on the internet. However, even today's most successful search engines struggle to provide high quality search results. According to recent studies as many as 50 percent of web search sessions fail to find any relevant results for the searcher. Researchers have proposed social search techniques, in which early searchers provide feedback that is used to improve relevance for later searchers. In this paper we investigate foundational questions of social search. In particular, we directly assess the degree of agreement among users about the relevance ranking of search results. We developed a simulated search engine interface that systematically randomizes Google's normal relevance ordering of the items presented to users. Our results show that (a) people are biased toward items in the top of the search lists, even if the list is randomized; (b) people explicit feedback is not biased and (c) people's shared preferences do not always agree with Google's result order. These results suggest that social search techniques might improve the effectiveness of web search engines.}, acmid = {1454052}, address = {New York, NY, USA}, author = {Agrahri, Arun Kumar and Manickam, Divya Anand Thattandi and Riedl, John}, booktitle = {Proceedings of the 2008 ACM conference on Recommender systems}, doi = {10.1145/1454008.1454052}, interhash = {507646c9d3219ac67da5f03fb2db303c}, intrahash = {77b2f9a1033c50acd438a48a1ecc0fa0}, isbn = {978-1-60558-093-7}, location = {Lausanne, Switzerland}, numpages = {4}, pages = {283--286}, publisher = {ACM}, title = {Can people collaborate to improve the relevance of search results?}, url = {http://doi.acm.org/10.1145/1454008.1454052}, year = 2008 } @inproceedings{mcnee2002recommending, abstract = {Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings matrix. Specifically, we tested the ability of collaborative filtering to recommend citations that would be suitable additional references for a target research paper. We investigated six algorithms for selecting citations, evaluating them through offline experiments against a database of over 186,000 research papers contained in ResearchIndex. We also performed an online experiment with over 120 users to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks. We found large differences in the accuracy of the algorithms in the offline experiment, especially when balanced for coverage. In the online experiment, users felt they received quality recommendations, and were enthusiastic about the idea of receiving recommendations in this domain.}, acmid = {587096}, address = {New York, NY, USA}, author = {McNee, Sean M. and Albert, Istvan and Cosley, Dan and Gopalkrishnan, Prateep and Lam, Shyong K. and Rashid, Al Mamunur and Konstan, Joseph A. and Riedl, John}, booktitle = {Proceedings of the 2002 ACM conference on Computer supported cooperative work}, doi = {10.1145/587078.587096}, interhash = {7178849aab57a025dff76e177d64be9b}, intrahash = {50f94e753fad76222bd33cbe591f9360}, isbn = {1-58113-560-2}, location = {New Orleans, Louisiana, USA}, numpages = {10}, pages = {116--125}, publisher = {ACM}, series = {CSCW '02}, title = {On the recommending of citations for research papers}, url = {http://doi.acm.org/10.1145/587078.587096}, year = 2002 } @inproceedings{McNee:2006:AEA:1125451.1125659, abstract = {Recommender systems have shown great potential to help users find interesting and relevant items from within a large information space. Most research up to this point has focused on improving the accuracy of recommender systems. We believe that not only has this narrow focus been misguided, but has even been detrimental to the field. The recommendations that are most accurate according to the standard metrics are sometimes not the recommendations that are most useful to users. In this paper, we propose informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies. We propose new user-centric directions for evaluating recommender systems.}, acmid = {1125659}, address = {New York, NY, USA}, author = {McNee, Sean M. and Riedl, John and Konstan, Joseph A.}, booktitle = {CHI '06 extended abstracts on Human factors in computing systems}, doi = {10.1145/1125451.1125659}, interhash = {fe396fbce5daacd374196ad688e3f149}, intrahash = {4b9fddbd766a9247856641989a778b23}, isbn = {1-59593-298-4}, location = {Montr\&\#233;al, Qu\&\#233;bec, Canada}, numpages = {5}, pages = {1097--1101}, publisher = {ACM}, series = {CHI EA '06}, title = {Being accurate is not enough: how accuracy metrics have hurt recommender systems}, url = {http://doi.acm.org/10.1145/1125451.1125659}, year = 2006 } @inproceedings{ekstrand2010automatically, abstract = {All new researchers face the daunting task of familiarizing themselves with the existing body of research literature in their respective fields. Recommender algorithms could aid in preparing these lists, but most current algorithms do not understand how to rate the importance of a paper within the literature, which might limit their effectiveness in this domain. We explore several methods for augmenting existing collaborative and content-based filtering algorithms with measures of the influence of a paper within the web of citations. We measure influence using well-known algorithms, such as HITS and PageRank, for measuring a node's importance in a graph. Among these augmentation methods is a novel method for using importance scores to influence collaborative filtering. We present a task-centered evaluation, including both an offline analysis and a user study, of the performance of the algorithms. Results from these studies indicate that collaborative filtering outperforms content-based approaches for generating introductory reading lists.}, acmid = {1864740}, address = {New York, NY, USA}, author = {Ekstrand, Michael D. and Kannan, Praveen and Stemper, James A. and Butler, John T. and Konstan, Joseph A. and Riedl, John T.}, booktitle = {Proceedings of the fourth ACM conference on Recommender systems}, doi = {10.1145/1864708.1864740}, interhash = {71ea85067f7d5f46bbb3a5da7e18ba34}, intrahash = {fbe0d5fca62781e5156d04e20d324a46}, isbn = {978-1-60558-906-0}, location = {Barcelona, Spain}, numpages = {8}, pages = {159--166}, publisher = {ACM}, title = {Automatically building research reading lists}, url = {http://doi.acm.org/10.1145/1864708.1864740}, year = 2010 } @inproceedings{conf/www/SenVR09, author = {Sen, Shilad and Vig, Jesse and Riedl, John}, booktitle = {WWW}, crossref = {conf/www/2009}, editor = {Quemada, Juan and León, Gonzalo and Maarek, Yoëlle S. and Nejdl, Wolfgang}, ee = {http://doi.acm.org/10.1145/1526709.1526800}, interhash = {4968b29a544394a5f9acd1bb8916e230}, intrahash = {8d38bdb12f6f2f89bd3c34d200e48b72}, isbn = {978-1-60558-487-4}, pages = {671-680}, publisher = {ACM}, title = {Tagommenders: connecting users to items through tags.}, url = {http://dblp.uni-trier.de/db/conf/www/www2009.html#SenVR09}, year = 2009 } @article{Herlocker:2004:ECF:963770.963772, 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.}, acmid = {963772}, address = {New York, NY, USA}, author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Terveen, Loren G. and Riedl, John T.}, doi = {10.1145/963770.963772}, interhash = {f8a70731d983634ac7105896d101c9d2}, intrahash = {c3a659108a568db1fba183c680dd1fd2}, issn = {1046-8188}, issue = {1}, journal = {ACM Trans. Inf. Syst.}, month = {January}, numpages = {49}, pages = {5--53}, privnote = {bla bla}, publisher = {ACM}, title = {Evaluating collaborative filtering recommender systems}, url = {http://doi.acm.org/10.1145/963770.963772}, volume = 22, year = 2004 } @inproceedings{sen2006tagging, abstract = {A tagging community's vocabulary of tags forms the basis for social navigation and shared expression.We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system.We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms 'effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.}, address = {New York, NY, USA}, author = {Sen, Shilad and Lam, Shyong K. and Rashid, Al Mamunur and Cosley, Dan and Frankowski, Dan and Osterhouse, Jeremy and Harper, F. Maxwell and Riedl, John}, booktitle = {CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work}, doi = {10.1145/1180875.1180904}, file = {sen2006tagging.pdf:sen2006tagging.pdf:PDF}, groups = {public}, interhash = {96b20bffcbc91e528461529935524b90}, intrahash = {582641c05e7a0b9396945a951822c83f}, isbn = {1-59593-249-6}, location = {Banff, Alberta, Canada}, pages = {181--190}, publisher = {ACM}, timestamp = {2011-02-02 15:10:48}, title = {tagging, communities, vocabulary, evolution}, url = {http://portal.acm.org/citation.cfm?id=1180904}, username = {dbenz}, year = 2006 } @inproceedings{lam2004shilling, abstract = {Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have become particularly common is in e-commerce, where recommendation of items can often help a customer find what she is interested in and, therefore can help drive sales. Unscrupulous producers in the never-ending quest for market penetration may find it profitable to shill recommender systems by lying to the systems in order to have their products recommended more often than those of their competitors. This paper explores four open questions that may affect the effectiveness of such shilling attacks: which recommender algorithm is being used, whether the application is producing recommendations or predictions, how detectable the attacks are by the operator of the system, and what the properties are of the items being attacked. The questions are explored experimentally on a large data set of movie ratings. Taken together, the results of the paper suggest that new ways must be used to evaluate and detect shilling attacks on recommender systems.}, address = {New York, NY, USA}, author = {Lam, Shyong K. and Riedl, John}, booktitle = {WWW '04: Proceedings of the 13th International Conference on World Wide Web}, doi = {10.1145/988672.988726}, interhash = {66e00212d44132e4d2ff6968a10999d4}, intrahash = {fa20593a49577529fdde250fc6d15110}, isbn = {1-58113-844-X}, location = {New York, NY, USA}, pages = {393--402}, publisher = {ACM}, title = {Shilling recommender systems for fun and profit}, url = {http://portal.acm.org/citation.cfm?id=988726&dl=GUIDE&coll=GUIDE&CFID=62005989&CFTOKEN=12250743}, year = 2004 } @inproceedings{vig2009tagsplanations, abstract = {While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.}, address = {New York, NY, USA}, author = {Vig, Jesse and Sen, Shilad and Riedl, John}, booktitle = {IUI '09: Proceedings of the 13th International Conference on Intelligent User Interfaces}, doi = {10.1145/1502650.1502661}, interhash = {dabcca79d5af180632a26ff292859671}, intrahash = {ed47adb106d45bb3ac20dd78c603532e}, isbn = {978-1-60558-168-2}, location = {Sanibel Island, Florida, USA}, pages = {47--56}, publisher = {ACM}, title = {Tagsplanations: explaining recommendations using tags}, url = {http://portal.acm.org/citation.cfm?id=1502661}, year = 2009 } @inproceedings{herlocker2000explaining, abstract = {Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address explanation interfaces for ACF systems - how they should be implemented and why they should be implemented. To explore how, we present a model for explanations based on the user's conceptual model of the recommendation process. We then present experimental results demonstrating what components of an explanation are the most compelling. To address why, we present experimental evidence that shows that providing explanations can improve the acceptance of ACF systems. We also describe some initial explorations into measuring how explanations can improve the filtering performance of users.}, address = {New York, NY, USA}, author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Riedl, John}, booktitle = {CSCW '00: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work}, doi = {10.1145/358916.358995}, interhash = {92273b87585b39bd394cb77f5a81ff1f}, intrahash = {85b8ec0aa805890a1e82156eebdb079b}, isbn = {1-58113-222-0}, location = {Philadelphia, Pennsylvania, United States}, pages = {241--250}, publisher = {ACM}, title = {Explaining collaborative filtering recommendations}, url = {http://portal.acm.org/citation.cfm?id=358995}, year = 2000 } @inproceedings{cosley2003believing, abstract = {Recommender systems use people's opinions about items in an information domain to help people choose other items. These systems have succeeded in domains as diverse as movies, news articles, Web pages, and wines. The psychological literature on conformity suggests that in the course of helping people make choices, these systems probably affect users' opinions of the items. If opinions are influenced by recommendations, they might be less valuable for making recommendations for other users. Further, manipulators who seek to make the system generate artificially high or low recommendations might benefit if their efforts influence users to change the opinions they contribute to the recommender. We study two aspects of recommender system interfaces that may affect users' opinions: the rating scale and the display of predictions at the time users rate items. We find that users rate fairly consistently across rating scales. Users can be manipulated, though, tending to rate toward the prediction the system shows, whether the prediction is accurate or not. However, users can detect systems that manipulate predictions. We discuss how designers of recommender systems might react to these findings.}, address = {New York, NY, USA}, author = {Cosley, Dan and Lam, Shyong K. and Albert, Istvan and Konstan, Joseph A. and Riedl, John}, booktitle = {CHI '03: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems}, doi = {10.1145/642611.642713}, interhash = {1b7ceacc5ada8aecc41e6684c0852702}, intrahash = {30230be1037c17a6ff958eb66b45d3a3}, isbn = {1-58113-630-7}, location = {Ft. Lauderdale, Florida, USA}, pages = {585--592}, publisher = {ACM}, title = {Is seeing believing?: how recommender system interfaces affect users' opinions}, url = {http://portal.acm.org/citation.cfm?id=642611.642713&type=series}, year = 2003 } @inproceedings{citeulike:965334, address = {New York, NY, USA}, author = {Sen, Shilad and Lam, Shyong K. and Rashid, Al M. and Cosley, Dan and Frankowski, Dan and Osterhouse, Jeremy and Harper, Maxwell F. and Riedl, John}, booktitle = {CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work}, citeulike-article-id = {965334}, doi = {10.1145/1180875.1180904}, interhash = {4f52a489eb6a696353d083b4d81b1fed}, intrahash = {25f189ea6ed05c1df70cd58a053b9798}, isbn = {1595932496}, pages = {181--190}, posted-at = {2009-04-01 05:03:08}, priority = {4}, publisher = {ACM Press}, title = {tagging, communities, vocabulary, evolution}, url = {http://dx.doi.org/10.1145/1180875.1180904}, year = 2006 } @inproceedings{sarwar2001item, abstract = {Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative filtering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative filtering techniques. Item-based techniques first analyze the user-item matrix to identify relationships between different items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze different item-based recommendation generation algorithms. We look into different techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and different techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available user-based algorithms.}, address = {New York, NY, USA}, author = {Sarwar, Badrul and Karypis, George and Konstan, Joseph and Riedl, John}, booktitle = {WWW '01: Proceedings of the 10th International Conference on World Wide Web}, doi = {10.1145/371920.372071}, interhash = {043d1aaba0f0b8c01d84edd517abedaf}, intrahash = {a6461157c8102d34b8001c7d33a42684}, isbn = {1-58113-348-0}, location = {Hong Kong}, pages = {285--295}, publisher = {ACM}, title = {Item-based collaborative filtering recommendation algorithms}, url = {http://portal.acm.org/citation.cfm?id=372071}, year = 2001 } @inproceedings{1502661, abstract = {While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.}, address = {New York, NY, USA}, author = {Vig, Jesse and Sen, Shilad and Riedl, John}, booktitle = {IUI '09: Proceedingsc of the 13th international conference on Intelligent user interfaces}, doi = {http://doi.acm.org/10.1145/1502650.1502661}, interhash = {a6d866cf13c75130c1969c9e40606fd1}, intrahash = {1e74fa227a24f49d8f6b17a02ea96db5}, isbn = {978-1-60558-168-2}, location = {Sanibel Island, Florida, USA}, pages = {47--56}, publisher = {ACM}, title = {Tagsplanations: explaining recommendations using tags}, url = {http://portal.acm.org/citation.cfm?id=1502650.1502661}, year = 2008 } @inproceedings{sen2006tagging, abstract = {A tagging community's vocabulary of tags forms the basis for social navigation and shared expression.We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system.We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms 'effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.}, address = {New York, NY, USA}, author = {Sen, Shilad and Lam, Shyong K. and Rashid, Al Mamunur and Cosley, Dan and Frankowski, Dan and Osterhouse, Jeremy and Harper, F. Maxwell and Riedl, John}, booktitle = {CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work}, doi = {http://doi.acm.org/10.1145/1180875.1180904}, interhash = {96b20bffcbc91e528461529935524b90}, intrahash = {582641c05e7a0b9396945a951822c83f}, isbn = {1-59593-249-6}, location = {Banff, Alberta, Canada}, pages = {181--190}, publisher = {ACM}, title = {tagging, communities, vocabulary, evolution}, url = {http://portal.acm.org/citation.cfm?id=1180904}, year = 2006 } @inproceedings{1180904, abstract = {A tagging community's vocabulary of tags forms the basis for social navigation and shared expression.We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system.We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms 'effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.}, address = {New York, NY, USA}, author = {Sen, Shilad and Lam, Shyong K. and Rashid, Al Mamunur and Cosley, Dan and Frankowski, Dan and Osterhouse, Jeremy and Harper, F. Maxwell and Riedl, John}, booktitle = {CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work}, doi = {http://doi.acm.org/10.1145/1180875.1180904}, interhash = {96b20bffcbc91e528461529935524b90}, intrahash = {582641c05e7a0b9396945a951822c83f}, isbn = {1-59593-249-6}, location = {Banff, Alberta, Canada}, pages = {181--190}, publisher = {ACM}, title = {tagging, communities, vocabulary, evolution}, url = {http://portal.acm.org/citation.cfm?id=1180904}, year = 2006 } @inproceedings{conf/www/SarwarKKR01, author = {Sarwar, Badrul M. and Karypis, George and Konstan, Joseph A. and Riedl, John}, booktitle = {WWW}, ee = {http://doi.acm.org/10.1145/371920.372071}, interhash = {043d1aaba0f0b8c01d84edd517abedaf}, intrahash = {f349b429624935212ebeed613b89794f}, pages = {285-295}, title = {Item-based collaborative filtering recommendation algorithms.}, url = {http://www10.org/cdrom/papers/pdf/p519.pdf}, year = 2001 } @inproceedings{1180904, abstract = {A tagging community's vocabulary of tags forms the basis for social navigation and shared expression.We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system.We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms 'effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.}, address = {New York, NY, USA}, author = {Sen, Shilad and Lam, Shyong K. and Rashid, Al Mamunur and Cosley, Dan and Frankowski, Dan and Osterhouse, Jeremy and Harper, F. Maxwell and Riedl, John}, booktitle = {CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work}, doi = {http://doi.acm.org/10.1145/1180875.1180904}, interhash = {96b20bffcbc91e528461529935524b90}, intrahash = {582641c05e7a0b9396945a951822c83f}, isbn = {1-59593-249-6}, location = {Banff, Alberta, Canada}, pages = {181--190}, publisher = {ACM}, title = {tagging, communities, vocabulary, evolution}, url = {http://portal.acm.org/citation.cfm?id=1180904}, year = 2006 }