@article{noy2008challenge, abstract = {The great success of Web 2.0 is mainly fuelled by an infrastructure that allows web users to create, share, tag, and connect content and knowledge easily. The tools for developing structured knowledge in this manner have started to appear as well. However, there are few, if any, user studies that are aimed at understanding what users expect from such tools, what works and what doesn't. We organized the Collaborative Knowledge Construction (CKC) Challenge to assess the state of the art for the tools that support collaborative processes for creation of various forms of structured knowledge. The goal of the Challenge was to get users to try out different tools and to learn what users expect from such tools-features that users need, features that they like or dislike. The Challenge task was to construct structured knowledge for a portal that would provide information about research. The Challenge design contained several incentives for users to participate. Forty-nine users registered for the Challenge; thirty-three of them participated actively by using the tools. We collected extensive feedback from the users where they discussed their thoughts on all the tools that they tried. In this paper, we present the results of the Challenge, discuss the features that users expect from tools for collaborative knowledge constructions, the features on which Challenge participants disagreed, and the lessons that we learned.}, author = {Noy, N F and Chugh, A and Alani, H}, doi = {10.1109/MIS.2008.14}, interhash = {df2e2abfd18d3b415d4b6a7cac970286}, intrahash = {98dcb79390913054e6255e605223f4b2}, journal = {IEEE Intell Syst}, month = {1}, number = 1, pages = {64-68}, pmid = {24683367}, title = {The CKC Challenge: Exploring Tools for Collaborative Knowledge Construction}, url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3966208/}, volume = 23, year = 2008 } @inproceedings{doerfel2014social, address = {New York, NY, USA}, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Proceedings of the 23rd International World Wide Web Conference}, interhash = {9223d6d728612c8c05a80b5edceeb78b}, intrahash = {11fab5468dd4b4e3db662ea5e68df8e0}, publisher = {ACM}, series = {WWW 2014}, title = {How Social is Social Tagging?}, year = 2014 } @inproceedings{benevenuto2009characterizing, abstract = {Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first of a kind analysis of user workloads in online social networks. Our study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we crawled the social network topology of Orkut, so that we could analyze user interaction data in light of the social graph. Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends' or non-immediate friends' pages. In summary, our analysis demonstrates the power of using clickstream data in identifying patterns in social network workloads and social interactions. Our analysis shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities. Consequently, compared to using only crawled data, considering silent interactions like browsing friends' pages increases the measured level of interaction among users.}, acmid = {1644900}, address = {New York, NY, USA}, author = {Benevenuto, Fabr\'{\i}cio and Rodrigues, Tiago and Cha, Meeyoung and Almeida, Virg\'{\i}lio}, booktitle = {Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference}, doi = {10.1145/1644893.1644900}, interhash = {ed9b10d4f36f90ddde9b95ce45b0b0be}, intrahash = {e5e25244e1ca2316a7871727e2df2bb9}, isbn = {978-1-60558-771-4}, location = {Chicago, Illinois, USA}, numpages = {14}, pages = {49--62}, publisher = {ACM}, series = {IMC '09}, title = {Characterizing User Behavior in Online Social Networks}, url = {http://doi.acm.org/10.1145/1644893.1644900}, year = 2009 } @article{jiang2013understanding, abstract = {Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques. In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs.}, acmid = {2517040}, address = {New York, NY, USA}, articleno = {18}, author = {Jiang, Jing and Wilson, Christo and Wang, Xiao and Sha, Wenpeng and Huang, Peng and Dai, Yafei and Zhao, Ben Y.}, doi = {10.1145/2517040}, interhash = {af18171c38a0b07fce62fb3fac5c6322}, intrahash = {aa9695f56135fd58de32b5b4a4c73698}, issn = {1559-1131}, issue_date = {October 2013}, journal = {ACM Trans. Web}, month = nov, number = 4, numpages = {39}, pages = {18:1--18:39}, publisher = {ACM}, title = {Understanding Latent Interactions in Online Social Networks}, url = {http://doi.acm.org/10.1145/2517040}, volume = 7, year = 2013 } @article{journals/corr/MitzlaffABHS13, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, ee = {http://arxiv.org/abs/1309.3888}, interhash = {40aa075d925f2e6e009986fd9e60b11b}, intrahash = {6f8017b9b01047d88b8e092747e25c4b}, journal = {CoRR}, title = {User-Relatedness and Community Structure in Social Interaction Networks.}, url = {http://dblp.uni-trier.de/db/journals/corr/corr1309.html#MitzlaffABHS13}, volume = {abs/1309.3888}, year = 2013 } @incollection{mitzlaff2013semantics, address = {Heidelberg, Germany}, author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas}, booktitle = {Complex Networks IV}, doi = {10.1007/978-3-642-36844-8_2}, editor = {Ghoshal, Gourab and Poncela-Casasnovas, Julia and Tolksdorf, Robert}, interhash = {bf333426bb7af5f01bf0c465c1bfe1fc}, intrahash = {0a35f1ed66fcd342a6a44d70c63fb735}, optisbn = {978-3-642-36843-1}, opturl = {http://dx.doi.org/10.1007/978-3-642-36844-8_2}, publisher = {Springer Verlag}, series = {Studies in Computational Intelligence}, title = {{Semantics of User Interaction in Social Media}}, volume = 476, year = 2013 } @incollection{MASH:13, address = {Heidelberg, Germany}, author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas}, booktitle = {Complex Networks IV}, doi = {10.1007/978-3-642-36844-8_2}, editor = {Ghoshal, Gourab and Poncela-Casasnovas, Julia and Tolksdorf, Robert}, interhash = {bf333426bb7af5f01bf0c465c1bfe1fc}, intrahash = {0a35f1ed66fcd342a6a44d70c63fb735}, optisbn = {978-3-642-36843-1}, opturl = {http://dx.doi.org/10.1007/978-3-642-36844-8_2}, publisher = {Springer Verlag}, series = {Studies in Computational Intelligence}, title = {{Semantics of User Interaction in Social Media}}, volume = 476, year = 2013 } @inproceedings{rose2004understanding, abstract = {Previous work on understanding user web search behavior has focused on how people search and what they are searching for, but not why they are searching. In this paper, we describe a framework for understanding the underlying goals of user searches, and our experience in using the framework to manually classify queries from a web search engine. Our analysis suggests that so-called navigational" searches are less prevalent than generally believed while a previously unexplored "resource-seeking" goal may account for a large fraction of web searches. We also illustrate how this knowledge of user search goals might be used to improve future web search engines.}, acmid = {988675}, address = {New York, NY, USA}, author = {Rose, Daniel E. and Levinson, Danny}, booktitle = {Proceedings of the 13th international conference on World Wide Web}, doi = {10.1145/988672.988675}, interhash = {684bf3f0c2e82239d3b2f932aa9a5ef4}, intrahash = {527fa40ab61aa9987608eed21e3d43eb}, isbn = {1-58113-844-X}, location = {New York, NY, USA}, numpages = {7}, pages = {13--19}, publisher = {ACM}, series = {WWW '04}, title = {Understanding user goals in web search}, url = {http://doi.acm.org/10.1145/988672.988675}, year = 2004 } @incollection{baglioni2003preprocessing, abstract = {We describe the web usage mining activities of an on-going project, called ClickWorld, that aims at extracting models of the navigational behaviour of a web site users. The models are inferred from the access logs of a web server by means of data and web mining techniques. The extracted knowledge is deployed to the purpose of offering a personalized and proactive view of the web services to users. We first describe the preprocessing steps on access logs necessary to clean, select and prepare data for knowledge extraction. Then we show two sets of experiments: the first one tries to predict the sex of a user based on the visited web pages, and the second one tries to predict whether a user might be interested in visiting a section of the site.}, author = {Baglioni, M. and Ferrara, U. and Romei, A. and Ruggieri, S. and Turini, F.}, booktitle = {AI*IA 2003: Advances in Artificial Intelligence}, doi = {10.1007/978-3-540-39853-0_20}, editor = {Cappelli, Amedeo and Turini, Franco}, interhash = {8dc29cedada5b8e9b3571ae77b983e2f}, intrahash = {1607f6c312b64832bab33fa843442d5e}, isbn = {978-3-540-20119-9}, pages = {237-249}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, title = {Preprocessing and Mining Web Log Data for Web Personalization}, url = {http://dx.doi.org/10.1007/978-3-540-39853-0_20}, volume = 2829, year = 2003 } @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{joachims2002optimizing, abstract = {This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches to learning retrieval functions from examples exist, they typically require training data generated from relevance judgments by experts. This makes them difficult and expensive to apply. The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking. Such clickthrough data is available in abundance and can be recorded at very low cost. Taking a Support Vector Machine (SVM) approach, this paper presents a method for learning retrieval functions. From a theoretical perspective, this method is shown to be well-founded in a risk minimization framework. Furthermore, it is shown to be feasible even for large sets of queries and features. The theoretical results are verified in a controlled experiment. It shows that the method can effectively adapt the retrieval function of a meta-search engine to a particular group of users, outperforming Google in terms of retrieval quality after only a couple of hundred training examples.}, acmid = {775067}, address = {New York, NY, USA}, author = {Joachims, Thorsten}, booktitle = {Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining}, doi = {10.1145/775047.775067}, interhash = {c78df69370bbf12636eaa5233b1fba83}, intrahash = {656a83f1057c5792506d0d656ae81d26}, isbn = {1-58113-567-X}, location = {Edmonton, Alberta, Canada}, numpages = {10}, pages = {133--142}, publisher = {ACM}, title = {Optimizing search engines using clickthrough data}, url = {http://doi.acm.org/10.1145/775047.775067}, year = 2002 } @inproceedings{joachims2005accurately, abstract = {This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average.}, acmid = {1076063}, address = {New York, NY, USA}, author = {Joachims, Thorsten and Granka, Laura and Pan, Bing and Hembrooke, Helene and Gay, Geri}, booktitle = {Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval}, doi = {10.1145/1076034.1076063}, interhash = {050982b76855a6b1258ed0b40cb69018}, intrahash = {8c488477626fa59db419ac77f3552029}, isbn = {1-59593-034-5}, location = {Salvador, Brazil}, numpages = {8}, pages = {154--161}, publisher = {ACM}, title = {Accurately interpreting clickthrough data as implicit feedback}, url = {http://doi.acm.org/10.1145/1076034.1076063}, year = 2005 } @inproceedings{morris2007searchtogether, abstract = {Studies of search habits reveal that people engage in many search tasks involving collaboration with others, such as travel planning, organizing social events, or working on a homework assignment. However, current Web search tools are designed for a single user, working alone. We introduce SearchTogether, a prototype that enables groups of remote users to synchronously or asynchronously collaborate when searching the Web. We describe an example usage scenario, and discuss the ways SearchTogether facilitates collaboration by supporting awareness, division of labor, and persistence. We then discuss the findings of our evaluation of SearchTogether, analyzing which aspects of its design enabled successful collaboration among study participants.}, acmid = {1294215}, address = {New York, NY, USA}, author = {Morris, Meredith Ringel and Horvitz, Eric}, booktitle = {Proceedings of the 20th annual ACM symposium on User interface software and technology}, doi = {10.1145/1294211.1294215}, interhash = {c6033afa6e8772439f50fde61c5efffa}, intrahash = {5c6932daaa447dc956385a1c322b1f6e}, isbn = {978-1-59593-679-0}, location = {Newport, Rhode Island, USA}, numpages = {10}, pages = {3--12}, publisher = {ACM}, title = {SearchTogether: an interface for collaborative web search}, url = {http://doi.acm.org/10.1145/1294211.1294215}, year = 2007 } @inproceedings{bai2011discovering, abstract = {Search engines rely upon crawling to build their Web page collections. A Web crawler typically discovers new URLs by following the link structure induced by links on Web pages. As the number of documents on the Web is large, discovering newly created URLs may take arbitrarily long, and depending on how a given page is connected to others, such a crawler may miss the pages altogether. In this paper, we evaluate the benefits of integrating a passive URL discovery mechanism into a Web crawler. This mechanism is passive in the sense that it does not require the crawler to actively fetch documents from the Web to discover URLs. We focus here on a mechanism that uses toolbar data as a representative source for new URL discovery. We use the toolbar logs of Yahoo! to characterize the URLs that are accessed by users via their browsers, but not discovered by Yahoo! Web crawler. We show that a high fraction of URLs that appear in toolbar logs are not discovered by the crawler. We also reveal that a certain fraction of URLs are discovered by the crawler later than the time they are first accessed by users. One important conclusion of our work is that web search engines can highly benefit from user feedback in the form of toolbar logs for passive URL discovery.}, acmid = {2063592}, address = {New York, NY, USA}, author = {Bai, Xiao and Cambazoglu, B. Barla and Junqueira, Flavio P.}, booktitle = {Proceedings of the 20th ACM international conference on Information and knowledge management}, doi = {10.1145/2063576.2063592}, interhash = {dfef0e1af73b9c9e5096a2118368ad21}, intrahash = {4e73c9d6ed79931ccdfcfda938e3be62}, isbn = {978-1-4503-0717-8}, location = {Glasgow, Scotland, UK}, numpages = {10}, pages = {77--86}, publisher = {ACM}, title = {Discovering URLs through user feedback}, url = {http://doi.acm.org/10.1145/2063576.2063592}, year = 2011 } @inproceedings{ls_leimeister, address = {Zürich, Switzerland}, author = {Ebel, P.}, booktitle = {Wirtschaftsinformatik 2011 (Poster Präsentation); 16. - 18. Februar 2011}, interhash = {112dad4d05171e016c107f0bd1a8b497}, intrahash = {b5aa1a0671cc92e1ffca67b4d906bbbf}, title = {Möglichkeiten zur IT-gestützten Durchführung von Lead User Workshops}, year = 2011 } @book{marcus.2011, address = {Berlin, Germany}, author = {Marcus, A.}, edition = 1, interhash = {d06c456d48edf66bf181fff1e832fdf9}, intrahash = {88d7fdac563b0181537ce9997247bde2}, publisher = {Springer}, title = {Design, User Experience, and Usability. Theory, Methods, Tools and Practice - First International Conference, DUXU 2011, Held as Part of HCI International 2011, Orlando, FL, USA, July 9-14, 2011, Proceedings, Part I}, year = 2011 } @proceedings{duxu.2011, abstract = {The two-volume set LNCS 6769 + LNCS 6770 constitutes the proceedings of the First International Conference on Design, User Experience, and Usability, DUXU 2011, held in Orlando, FL, USA in July 2011 in the framework of the 14th International Conference on Human-Computer Interaction, HCII 2011, incorporating 12 thematically similar conferences. A total of 4039 contributions was submitted to HCII 2011, of which 1318 papers were accepted for publication. The total of 154 contributions included in the DUXU proceedings were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on DUXU theory, methods and tools; DUXU guidelines and standards; novel DUXU: devices and their user interfaces; DUXU in industry; DUXU in the mobile and vehicle context; DXU in Web environment; DUXU and ubiquitous interaction/appearance; DUXU in the development and usage lifecycle; DUXU evaluation; and DUXU beyond usability: culture, branding, and emotions.}, address = {Berlin}, booktitle = {HCI (10)}, editor = {Marcus, A.}, interhash = {b1c6c6a36b7ea48bed349cfb2f16e76e}, intrahash = {edf738a092e9cce7afd0789f8f22578a}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Design, User Experience, and Usability. Theory, Methods, Tools and Practice - First International Conference, DUXU 2011, Held as Part of HCI International 2011, Orlando, FL, USA, July 9-14, 2011, Proceedings, Part II}, url = {http://dblp.uni-trier.de/db/conf/hci/hci2011-10.html}, volume = 6770, year = 2011 } @proceedings{duxu2.2011, abstract = {The two-volume set LNCS 6769 + LNCS 6770 constitutes the proceedings of the First International Conference on Design, User Experience, and Usability, DUXU 2011, held in Orlando, FL, USA in July 2011 in the framework of the 14th International Conference on Human-Computer Interaction, HCII 2011, incorporating 12 thematically similar conferences. A total of 4039 contributions was submitted to HCII 2011, of which 1318 papers were accepted for publication. The total of 154 contributions included in the DUXU proceedings were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on DUXU theory, methods and tools; DUXU guidelines and standards; novel DUXU: devices and their user interfaces; DUXU in industry; DUXU in the mobile and vehicle context; DXU in Web environment; DUXU and ubiquitous interaction/appearance; DUXU in the development and usage lifecycle; DUXU evaluation; and DUXU beyond usability: culture, branding, and emotions.}, address = {Berlin}, booktitle = {HCI (10)}, editor = {Marcus, A.}, interhash = {bc439f5d7a35575b2e9c1d0bc73a88d7}, intrahash = {1efd5fc04c4c10399491e141273fb688}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Design, User Experience, and Usability. Theory, Methods, Tools and Practice - First International Conference, DUXU 2011, Held as Part of HCI International 2011, Orlando, FL, USA, July 9-14, 2011, Proceedings, 2 Part II}, url = {http://dblp.uni-trier.de/db/conf/hci/hci2011-10.html}, volume = 6770, year = 2011 } @inproceedings{mcnee2006stupid, abstract = {If recommenders are to help people be more productive, they need to support a wide variety of real-world information seeking tasks, such as those found when seeking research papers in a digital library. There are many potential pitfalls, including not knowing what tasks to support, generating recommendations for the wrong task, or even failing to generate any meaningful recommendations whatsoever. We posit that different recommender algorithms are better suited to certain information seeking tasks. In this work, we perform a detailed user study with over 130 users to understand these differences between recommender algorithms through an online survey of paper recommendations from the ACM Digital Library. We found that pitfalls are hard to avoid. Two of our algorithms generated 'atypical' recommendations recommendations that were unrelated to their input baskets. Users reacted accordingly, providing strong negative results for these algorithms. Results from our 'typical' algorithms show some qualitative differences, but since users were exposed to two algorithms, the results may be biased. We present a wide variety of results, teasing out differences between algorithms. Finally, we succinctly summarize our most striking results as "Don't Look Stupid" in front of users.}, acmid = {1180903}, address = {New York, NY, USA}, author = {McNee, Sean M. and Kapoor, Nishikant and Konstan, Joseph A.}, booktitle = {Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work}, doi = {10.1145/1180875.1180903}, interhash = {24be686d042a3a4a710d9ff22dee0f2e}, intrahash = {7775150ca225770019bd94db9be5db40}, isbn = {1-59593-249-6}, location = {Banff, Alberta, Canada}, numpages = {10}, pages = {171--180}, publisher = {ACM}, series = {CSCW '06}, title = {Don't look stupid: avoiding pitfalls when recommending research papers}, url = {http://doi.acm.org/10.1145/1180875.1180903}, year = 2006 } @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 }