Number of co-authors:76
Number of publications with 3 favourite co-authors:Robert Cooley:Yan Chen:Sherry Xin Li:
Joseph A. Konstan's 3 most productive colleagues in number of publications:Robert E. Kraut:98Loren Terveen:69John Riedl:61
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Joseph A. Konstan
Has also published under the name of:
"J. A. Konstan" and "Joseph Konstan"
Personal Homepage: www-users.cs.umn.edu/~konstan/
Joseph A. Konstan is Distinguished McKnight University Professor and Associate Department Head at the Department of Computer Science and Engineering, University of Minneso.
Publications by Joseph A. Konstan (bibliography)
Dong, Xiao, Harper, F. Maxwell and Konstan, Joseph A. (2011): Entity-linking interfaces in user-contributed content: preference and performance. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 2187-2196. Available online
The ability to embed links to other resources in user generated content can help authors create more useful and usable content. A variety of interfaces have emerged for entity-linking at popular online sites; such interfaces vary in the way that entity linking is initiated (in-band or out-of-band with respect to the message creation), the timing of entity resolution (interrupting or deferred), and the method of resolving the entity (auto-completion or search). Four interfaces mimicking popular entity linking websites were developed and tested. Results showed that out-of-band initiation (e.g., a link button) was faster to learn, but that in-band initiation performance improved with familiarity. Deferred search was disliked and led to worse performance. And auto-completion was generally preferred to search interfaces.
© All rights reserved Dong et al. and/or their publisher
Ekstrand, Michael D., Ludwig, Michael, Konstan, Joseph A. and Riedl, John T. (2011): Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit. In: Proceedings of the 2011 ACM Conference on Recommender Systems 2011. pp. 133-140. Available online
Recommender systems research is being slowed by the difficulty of replicating and comparing research results. Published research uses various experimental methodologies and metrics that are difficult to compare. It also often fails to sufficiently document the details of proposed algorithms or the evaluations employed. Researchers waste time reimplementing well-known algorithms, and the new implementations may miss key details from the original algorithm or its subsequent refinements. When proposing new algorithms, researchers should compare them against finely-tuned implementations of the leading prior algorithms using state-of-the-art evaluation methodologies. With few exceptions, published algorithmic improvements in our field should be accompanied by working code in a standard framework, including test harnesses to reproduce the described results. To that end, we present the design and freely distributable source code of LensKit, a flexible platform for reproducible recommender systems research. LensKit provides carefully tuned implementations of the leading collaborative filtering algorithms, APIs for common recommender system use cases, and an evaluation framework for performing reproducible offline evaluations of algorithms. We demonstrate the utility of LensKit by replicating and extending a set of prior comparative studies of recommender algorithms -- showing limitations in some of the original results -- and by investigating a question recently raised by a leader in the recommender systems community on problems with error-based prediction evaluation.
© All rights reserved Ekstrand et al. and/or ACM Press
Pal, Aditya, Farzan, Rosta, Konstan, Joseph A. and Kraut, Robert E. (2011): Early Detection of Potential Experts in Question Answering Communities. In: Proceedings of the 2011 Conference on User Modeling, Adaptation and Personalization 2011. pp. 231-242. Available online
Question answering communities (QA) are sustained by a handful of experts who provide a large number of high quality answers. Identifying these experts during the first few weeks of their joining the community can be beneficial as it would allow community managers to take steps to develop and retain these potential experts. In this paper, we explore approaches to identify potential experts as early as within the first two weeks of their association with the QA. We look at users' behavior and estimate their motivation and ability to help others. These qualities enable us to build classification and ranking models to identify users who are likely to become experts in the future. Our results indicate that the current experts can be effectively identified from their early behavior. We asked community managers to evaluate the potential experts identified by our algorithm and their analysis revealed that quite a few of these users were already experts or on the path of becoming experts. Our retrospective analysis shows that some of these potential experts had already left the community, highlighting the value of early identification and engagement.
© All rights reserved Pal et al. and/or their publisher
Resnick, Paul, Konstan, Joseph A., Hotho, Andreas and Pindado, Jesus (2010): Contests: way forward or detour?. In: Proceedings of the 2010 ACM Conference on Recommender Systems 2010. pp. 37-38. Available online
Contests and challenges have energized researchers and focused attention in many fields recently, including recommender systems. At the 2008 RecSys conference, winners were announced for a contest proposing new startup companies. The 2009 conference featured a panel reflecting on the then recently completed Netflix challenge. Would additional contests help move the field of recommender systems forward? Or would they just draw attention from the most important problems to problems that are most easily formulated as contests? If contests would be useful, what should the tasks be and how should performance be evaluated? The panel will begin with short presentations by the panelists. Following that, the panelists will respond to brief sketches of possible new contests. In addition to prediction and ranking tasks, tasks might include making creative use of the outputs of a fixed recommender engine, or eliciting inputs for a recommender engine.
© All rights reserved Resnick et al. and/or ACM Press
Ekstrand, Michael D., Kannan, Praveen, Stemper, James A., Butler, John T., Konstan, Joseph A. and Riedl, John T. (2010): Automatically building research reading lists. In: Proceedings of the 2010 ACM Conference on Recommender Systems 2010. pp. 159-166. Available online
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.
© All rights reserved Ekstrand et al. and/or ACM Press
Harper, F. Maxwell, Moy, Daniel and Konstan, Joseph A. (2009): Facts or friends?: distinguishing informational and conversational questions in social Q&A sites. In: Proceedings of ACM CHI 2009 Conference on Human Factors in Computing Systems 2009. pp. 759-768. Available online
Tens of thousands of questions are asked and answered every day on social question and answer (Q&A) Web sites such as Yahoo Answers. While these sites generate an enormous volume of searchable data, the problem of determining which questions and answers are archival quality has grown. One major component of this problem is the prevalence of conversational questions, identified both by Q&A sites and academic literature as questions that are intended simply to start discussion. For example, a conversational question such as "do you believe in evolution?" might successfully engage users in discussion, but probably will not yield a useful web page for users searching for information about evolution. Using data from three popular Q&A sites, we confirm that humans can reliably distinguish between these conversational questions and other informational questions, and present evidence that conversational questions typically have much lower potential archival value than informational questions. Further, we explore the use of machine learning techniques to automatically classify questions as conversational or informational, learning in the process about categorical, linguistic, and social differences between different question types. Our algorithms approach human performance, attaining 89.7% classification accuracy in our experiments.
© All rights reserved Harper et al. and/or ACM Press
Harper, F. Maxwell, Raban, Daphne, Rafaeli, Sheizaf and Konstan, Joseph A. (2008): Predictors of answer quality in online Q&A sites. In: Proceedings of ACM CHI 2008 Conference on Human Factors in Computing Systems April 5-10, 2008. pp. 865-874. Available online
Question and answer (Q&A) sites such as Yahoo! Answers are places where users ask questions and others answer them. In this paper, we investigate predictors of answer quality through a comparative, controlled field study of responses provided across several online Q&A sites. Along with several quantitative results concerning the effects of factors such as question topic and rhetorical strategy, we present two high-level messages. First, you get what you pay for in Q&A sites. Answer quality was typically higher in Google Answers (a fee-based site) than in the free sites we studied, and paying more money for an answer led to better outcomes. Second, we find that a Q&A site's community of users contributes to its success. Yahoo! Answers, a Q&A site where anybody can answer questions, outperformed sites that depend on specific individuals to answer questions, such as library reference services.
© All rights reserved Harper et al. and/or ACM Press
Krishnan, Vinod, Narayanashetty, Pradeep Kumar, Nathan, Mukesh, Davies, Richard T. and Konstan, Joseph A. (2008): Who predicts better?: results from an online study comparing humans and an online recommender system. In: Proceedings of the 2008 ACM Conference on Recommender Systems 2008. pp. 211-218. Available online
Algorithmic recommender systems attempt to predict which items a target user will like based on information about the user's prior preferences and the preferences of a larger community. After more than a decade of widespread use, researchers and system users still debate whether such "impersonal" recommender systems actually perform as well as human recommenders. We compare the performance of MovieLens algorithmic predictions with the recommendations made, based on the same user profiles, by active MovieLens users. We found that algorithmic collaborative filtering outperformed humans on average, though some individuals outperformed the system substantially and humans on average outperformed the system on certain prediction tasks.
© All rights reserved Krishnan et al. and/or ACM Press
Good, Nathaniel, Grossklags, Jens, Mulligan, Deirdre K. and Konstan, Joseph A. (2007): Noticing notice: a large-scale experiment on the timing of software license agreements. In: Proceedings of ACM CHI 2007 Conference on Human Factors in Computing Systems 2007. pp. 607-616. Available online
Spyware is an increasing problem. Interestingly, many programs carrying spyware honestly disclose the activities of the software, but users install the software anyway. We report on a study of software installation to assess the effectiveness of different notices for helping people make better decisions on which software to install. Our study of 222 users showed that providing a short summary notice, in addition to the End User License Agreement (EULA), before the installation reduced the number of software installations significantly. We also found that providing the short summary notice after installation led to a significant number of uninstalls. However, even with the short notices, many users installed the program and later expressed regret for doing so. These results, along with a detailed analysis of installation, regret, and survey data about user behaviors informs our recommendations to policymakers and designers for assessing the "adequacy" of consent in the context of software that exhibits behaviors associated with spyware.
© All rights reserved Good et al. and/or ACM Press
Weisz, Justin D., Kiesler, Sara, Zhang, Hui, Ren, Yuqing, Kraut, Robert E. and Konstan, Joseph A. (2007): Watching together: integrating text chat with video. In: Proceedings of ACM CHI 2007 Conference on Human Factors in Computing Systems 2007. pp. 877-886. Available online
Watching video online is becoming increasingly popular, and new video streaming technologies have the potential to transform video watching from a passive, isolating experience into an active, socially engaging experience. However, the viability of an active social experience is unclear: both chatting and watching video require attention, and may interfere with one another and detract from the experience. In this paper, we empirically examine the activity of chatting while watching video online. We examine how groups of friends and strangers interact, and find that chat has a positive influence on social relationships, and people chat despite being distracted. We discuss the benefits and opportunities provided by mixing chat and video, uncover some of the attentional and social challenges inherent in this combination of media, and provide guidance for structuring the viewing experience.
© All rights reserved Weisz et al. and/or ACM Press
Harper, F. Maxwell, Li, Sherry Xin, Chen, Yan and Konstan, Joseph A. (2007): Social Comparisons to Motivate Contributions to an Online Community. In: Kort, Yvonne de, IJsselsteijn, Wijnand, Midden, Cees J. H., Eggen, Berry and Fogg, B. J. (eds.) PERSUASIVE 2007 - Persuasive Technology, Second International Conference on Persuasive Technology April 26-27, 2007, Palo Alto, CA, USA. pp. 148-159. Available online
Kapoor, Nishikant, Chen, Jilin, Butler, John T., Fouty, Gary C., Stemper, James A., Riedl, John and Konstan, Joseph A. (2007): TechLens: a researcher's desktop. In: Proceedings of the 2007 ACM Conference on Recommender Systems 2007. pp. 183-184. Available online
Rapid and continuous growth of digital libraries, coupled with brisk advancements in technology, has driven users to seek tools and services that are not only customized to their specific needs, but are also helpful in keeping them stay abreast with the latest developments in their field. TechLens is a recommender system that learns about its users through implicit feedback, builds correlations among them, and uses that information to generate recommendations that match the user's profile. It gives users control over which parts of their profile of known citations are used in forming recommendations for new articles. This demonstration is a prototype that showcases some of the tools and services that TechLens offers to the users of digital libraries.
© All rights reserved Kapoor et al. and/or ACM Press
McNee, Sean M., Kapoor, Nishikant and Konstan, Joseph A. (2006): Don't look stupid: avoiding pitfalls when recommending research papers. In: Proceedings of ACM CSCW06 Conference on Computer-Supported Cooperative Work 2006. pp. 171-180. Available online
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.
© All rights reserved McNee et al. and/or ACM Press
Kapoor, Nishikant, Konstan, Joseph A. and Terveen, Loren (2005): How peer photos influence member participation in online communities. In: Proceedings of ACM CHI 2005 Conference on Human Factors in Computing Systems 2005. pp. 1525-1528. Available online
Online communities (OLCs) are gatherings of like-minded people, brought together in cyberspace by shared interests. Creating such communities is not a big challenge; sustaining members' participation is. In this paper, we describe a technique for presenting members' photos and evaluate how it affects member participation in the community. We compare three different policies for presenting peer photos on the home page of the web site. Our results show that explicit requests in the form of simple and short messages on the home page of a community can induce participation. We show that we were able to motivate members to (a) log into the system to see photos of fellow members, and (b) upload their personal photos.
© All rights reserved Kapoor et al. and/or ACM Press
Good, Nathaniel, Dhamija, Rachna, Grossklags, Jens, Thaw, David, Aronowitz, Steven, Mulligan, Deirdre and Konstan, Joseph A. (2005): Stopping spyware at the gate: a user study of privacy, notice and spyware. In: Proceedings of the 2005 Symposium on Usable Privacy and Security 2005. pp. 43-52. Available online
Spyware is a significant problem for most computer users. The term "spyware" loosely describes a new class of computer software. This type of software may track user activities online and offline, provide targeted advertising and/or engage in other types of activities that users describe as invasive or undesirable. While the magnitude of the spyware problem is well documented, recent studies have had only limited success in explaining the broad range of user behaviors that contribute to the proliferation of spyware. As opposed to viruses and other malicious code, users themselves often have a choice whether they want to install these programs. In this paper, we discuss an ecological study of users installing five real world applications. In particular, we seek to understand the influence of the form and content of notices (e.g., EULAs) on user's installation decisions. Our study indicates that while notice is important, notice alone may not be enough to affect users' decisions to install an application. We found that users have limited understanding of EULA content and little desire to read lengthy notices. Users found short, concise notices more useful, and noticed them more often, yet they did not have a significant effect on installation for our population. When users were informed of the actual contents of the EULAs to which they agreed, we found that users often regret their installation decisions. We discovered that regardless of the bundled content, users will often install an application if they believe the utility is high enough. However, we discovered that privacy and security become important factors when choosing between two applications with similar functionality. Given two similar programs (e.g. KaZaA and Edonkey), consumers will choose the one they believe to be less invasive and more stable. We also found that providing vague information in EULAs and short notices can create an unwarranted impression of increased security. In these cases, it may be helpful to have a standardized format for assessing the possible options and trade-offs between applications.
© All rights reserved Good et al. and/or ACM Press
Harper, F. Maxwell, Li, Sherry Xin, Chen, Yan and Konstan, Joseph A. (2005): An Economic Model of User Rating in an Online Recommender System. In: Ardissono, Liliana, Brna, Paul and Mitrovic, Antonija (eds.) User Modeling 2005 - 10th International Conference - UM 2005 July 24-29, 2005, Edinburgh, Scotland, UK. pp. 307-316. Available online
Ziegler, Cai-Nicolas, McNee, Sean M., Konstan, Joseph A. and Lausen, Georg (2005): Improving recommendation lists through topic diversification. In: Proceedings of the 2005 International Conference on the World Wide Web 2005. pp. 22-32. Available online
In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm. Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361,349 ratings and an online study involving more than 2,100 subjects.
© All rights reserved Ziegler et al. and/or ACM Press
Herlocker, Jonathan L., Konstan, Joseph A., Terveen, Loren and Riedl, John (2004): Evaluating collaborative filtering recommender systems. In ACM Transactions on Information Systems, 22 (1) pp. 5-53. Available online
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.
© All rights reserved Herlocker et al. and/or ACM Press
Miller, Bradley N., Konstan, Joseph A. and Riedl, John (2004): PocketLens: Toward a personal recommender system. In ACM Transactions on Information Systems, 22 (3) pp. 437-476. Available online
Recommender systems using collaborative filtering are a popular technique for reducing information overload and finding products to purchase. One limitation of current recommenders is that they are not portable. They can only run on large computers connected to the Internet. A second limitation is that they require the user to trust the owner of the recommender with personal preference data. Personal recommenders hold the promise of delivering high quality recommendations on palmtop computers, even when disconnected from the Internet. Further, they can protect the user's privacy by storing personal information locally, or by sharing it in encrypted form. In this article we present the new PocketLens collaborative filtering algorithm along with five peer-to-peer architectures for finding neighbors. We evaluate the architectures and algorithms in a series of offline experiments. These experiments show that Pocketlens can run on connected servers, on usually connected workstations, or on occasionally connected portable devices, and produce recommendations that are as good as the best published algorithms to date.
© All rights reserved Miller et al. and/or ACM Press
Bailey, Brian P. and Konstan, Joseph A. (2003): Are informal tools better?: comparing DEMAIS, pencil and paper, and authorware for early multimedia design. In: Cockton, Gilbert and Korhonen, Panu (eds.) Proceedings of the ACM CHI 2003 Human Factors in Computing Systems Conference April 5-10, 2003, Ft. Lauderdale, Florida, USA. pp. 313-320.
Cosley, Dan, Lam, Shyong K., Albert, Istvan, Konstan, Joseph A. and Riedl, John (2003): Is seeing believing?: how recommender system interfaces affect users' opinions. In: Cockton, Gilbert and Korhonen, Panu (eds.) Proceedings of the ACM CHI 2003 Human Factors in Computing Systems Conference April 5-10, 2003, Ft. Lauderdale, Florida, USA. pp. 585-592.
Miller, Bradley N., Albert, Istvan, Lam, Shyong K., Konstan, Joseph A. and Riedl, John (2003): MovieLens unplugged: experiences with an occasionally connected recommender system. In: Johnson, Lewis and Andre, Elisabeth (eds.) International Conference on Intelligent User Interfaces 2003 January 12-15, 2003, Miami, Florida, USA. pp. 263-266. Available online
Recommender systems have changed the way people shop online. Recommender systems on wireless mobile devices may have the same impact on the way people shop in stores. We present our experience with implementing a recommender system on a PDA that is occasionally connected to the network. This interface helps users of the MovieLens movie recommendation service select movies to rent, buy, or see while away from their computer. The results of a nine month field study show that although there are several challenges to overcome, mobile recommender systems have the potential to provide value to their users today.
© All rights reserved Miller et al. and/or ACM Press
McNee, Sean, Lam, Shyong, Guetzlaff, Catherine, Konstan, Joseph A. and Riedl, John (2003): Confidence Displays and Training in Recommender Systems. In: Proceedings of IFIP INTERACT03: Human-Computer Interaction 2003, Zurich, Switzerland. p. 176.
Miller, B. N., Albert, I., Lam, S. K., Konstan, Joseph A. and Riedl, John (2003): MovieLens Unplugged: Experiences with a Recommender System on Four Mobile Devices. In: Proceedings of the HCI03 Conference on People and Computers XVII 2003. pp. 263-280.
McNee, Sean M., Lam, Shyong K., Konstan, Joseph A. and Riedl, John (2003): Interfaces for Eliciting New User Preferences in Recommender Systems. In: Brusilovsky, Peter, Corbett, Albert T. and Rosis, Fiorella De (eds.) User Modeling 2003 - 9th International Conference - UM 2003 June 22-26, 2003, Johnstown, PA, USA. pp. 178-187. Available online
McNee, Sean M., Albert, Istvan, Cosley, Dan, Gopalkrishnan, Prateep, Lam, Shyong K., Rashid, Al Mamunur, Konstan, Joseph A. and Riedl, John (2002): On the recommending of citations for research papers. In: Churchill, Elizabeth F., McCarthy, Joe, Neuwirth, Christine and Rodden, Tom (eds.) Proceedings of the 2002 ACM conference on Computer supported cooperative work November 16 - 20, 2002, New Orleans, Louisiana, USA. pp. 116-125. Available online
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.
© All rights reserved McNee et al. and/or ACM Press
Rashid, Al Mamunur, Albert, Istvan, Cosley, Dan, Lam, Shyong K., McNee, Sean M., Konstan, Joseph A. and Riedl, John (2002): Getting to know you: learning new user preferences in recommender systems. In: Gil, Yolanda and Leake, David (eds.) International Conference on Intelligent User Interfaces 2002 January 13-16, 2002, San Francisco, California, USA. pp. 127-134. Available online
Recommender systems have become valuable resources for users seeking intelligent ways to search through the enormous volume of information available to them. One crucial unsolved problem for recommender systems is how best to learn about a new user. In this paper we study six techniques that collaborative filtering recommender systems can use to learn about new users. These techniques select a sequence of items for the collaborative filtering system to present to each new user for rating. The techniques include the use of information theory to select the items that will give the most value to the recommender system, aggregate statistics to select the items the user is most likely to have an opinion about, balanced techniques that seek to maximize the expected number of bits learned per presented item, and personalized techniques that predict which items a user will have an opinion about. We study the techniques thru offline experiments with a large pre-existing user data set, and thru a live experiment with over 300 users. We show that the choice of learning technique significantly affects the user experience, in both the user effort and the accuracy of the resulting predictions.
© All rights reserved Rashid et al. and/or ACM Press
Schafer, J. Ben, Konstan, Joseph A. and Riedl, John (2002): Meta-recommendation systems: user-controlled integration of diverse recommendations. In: Proceedings of the 2002 ACM CIKM International Conference on Information and Knowledge Management November 4-9, 2002, McLean, VA, USA. pp. 43-51. Available online
Connor, M. O., Cosley, Dan, Konstan, Joseph A. and Riedl, John (2001): PolyLens: A recommender system for groups of user. In: Ecscw 2001 - Proceedings of the Seventh European Conference on Computer Supported Cooperative Work 16-20 September, 2001, Bonn, Germany. pp. 199-218.
Bailey, Brian P., Konstan, Joseph A. and Carlis, J. V. (2001): The Effects of Interruptions on Task Performance, Annoyance, and Anxiety in the User Interface. In: Proceedings of IFIP INTERACT01: Human-Computer Interaction 2001, Tokyo, Japan. pp. 593-601.
Konstan, Joseph A. (2001): Heavyweight Applications of Lightweight User Models: A Look at Collaborative Filtering, Recommender Systems, and Real-Time Personalization. In: Bauer, Mathias, Gmytrasiewicz, Piotr J. and Vassileva, Julita (eds.) User Modeling 2001 - 8th International Conference - UM 2001 July 13-17, 2001, Sonthofen, Germany. p. 314. Available online
Herlocker, Jonathan L. and Konstan, Joseph A. (2001): Content-Independent Task-Focused Recommendation. In IEEE Internet Computing, 5 (6) pp. 40-47. Available online
Bailey, Brian P., Konstan, Joseph A. and Carlis, John V. (2001): DEMAIS: designing multimedia applications with interactive storyboards. In: ACM Multimedia 2001 2001. pp. 241-250. Available online
Herlocker, Jonathan L., Konstan, Joseph A. and Riedl, John (2000): Explaining Collaborative Filtering Recommendations. In: Kellogg, Wendy A. and Whittaker, Steve (eds.) Proceedings of the 2000 ACM conference on Computer supported cooperative work 2000, Philadelphia, Pennsylvania, United States. pp. 241-250. Available online
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.
© All rights reserved Herlocker et al. and/or ACM Press
Konstan, Joseph A. (1999): New Beginnings. In ACM SIGCHI Bulletin, 31 (4) pp. 1-2. Available online
Herlocker, Jonathan L., Konstan, Joseph A., Borchers, Al and Riedl, John (1999): An Algorithmic Framework for Performing Collaborative Filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 1999. pp. 230-237. Available online
Sarwar, Badrul M., Konstan, Joseph A., Borchers, Al, Herlocker, Jonathan L., Miller, Brad and Riedl, John (1998): Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System. In: Poltrock, Steven and Grudin, Jonathan (eds.) Proceedings of the 1998 ACM conference on Computer supported cooperative work November 14 - 18, 1998, Seattle, Washington, United States. pp. 345-354. Available online
Collaborative filtering systems help address information overload by using the opinions of users in a community to make personal recommendations for documents to each user. Many collaborative filtering systems have few user opinions relative to the large number of documents available. This sparsity problem can reduce the utility of the filtering system by reducing the number of documents for which the system can make recommendations and adversely affecting the quality of recommendations. This paper defines and implements a model for integrating content-based ratings into a collaborative filtering system. The filterbot model allows collaborative filtering systems to address sparsity by tapping the strength of content filtering techniques. We identify and evaluate metrics for assessing the effectiveness of filterbots specifically, and filtering system enhancements in general. Finally, we experimentally validate the filterbot approach by showing that even simple filterbots such as spell checking can increase the utility for users of sparsely populated collaborative filtering systems.
© All rights reserved Sarwar et al. and/or ACM Press
Carlis, John V. and Konstan, Joseph A. (1998): Interactive Visualization of Serial Periodic Data. In: Mynatt, Elizabeth D. and Jacob, Robert J. K. (eds.) Proceedings of the 11th annual ACM symposium on User interface software and technology November 01 - 04, 1998, San Francisco, California, United States. pp. 29-38. Available online
Serial periodic data exhibit both serial and periodic properties. For example, time continues forward serially, but weeks, months, and years are periods that recur. While there are extensive visualization techniques for exploring serial data, and a few for exploring periodic data, no existing technique simultaneously displays serial and periodic attributes of a data set. We introduce a spiral visualization technique, which displays data along a spiral to highlight serial attributes along the spiral axis and periodic ones along the radii. We show several applications of the spiral visualization to data exploration tasks, present our implementation, discuss the capacity for data analysis, and present findings of our informal study with users in data-rich scientific domains.
© All rights reserved Carlis and Konstan and/or ACM Press
Gustafson, Tara, Schafer, J. Ben and Konstan, Joseph A. (1998): Agents in their Midst: Evaluating User Adaptation to Agent-Assisted Interfaces. In: Marks, Joe (ed.) International Conference on Intelligent User Interfaces 1998 January 6-9, 1998, San Francisco, California, USA. pp. 163-170. Available online
This paper presents the results of introducing an agent into a real-world work situation -- production of the online edition of a daily newspaper. Quantitative results show that agents helped users accomplish the task more rapidly without increasing user error and that users consistently underestimated the quality of their own performance. Qualitative results show that users accepted agents rapidly and that they unknowingly altered their working styles to adapt to the agent.
© All rights reserved Gustafson et al. and/or ACM Press
Konstan, Joseph A. and Siegel, Jane (1998): Unifying HCI: The Impossible Possibility: The CHI 98 Basic Research Symposium. In ACM SIGCHI Bulletin, 30 (4) pp. 30-32. Available online
On April 19 and 20, for the seventh consecutive year, a group of researchers from the CHI community gathered for a symposium devoted to fundamental issues in research. It can be both invigorating and intimidating to attempt to capture two days worth of vigorous discussion in a few pages. We know we cannot fully succeed, but it revives the spirit of the event to try. In this article, we attempt to relate some of the background of the event, its structure and formal content, and a glimpse at the discussions and interactions that filled two days.
© All rights reserved Konstan and Siegel and/or ACM Press
Bailey, Brian P., Konstan, Joseph A., Cooley, Robert and Dejong, Moses (1998): Nsync - A Toolkit for Building Interactive Multimedia Presentations. In: ACM Multimedia 1998 1998. pp. 257-266. Available online
Chi, Ed Huai-hsin, Riedl, John, Barry, Phillip and Konstan, Joseph A. (1998): Principles for Information Visualization Spreadsheets. In IEEE Computer Graphics and Applications, 18 (4) pp. 30-38. Available online
Chi, Ed H., Konstan, Joseph A., Barry, Phillip and Riedl, John (1997): A Spreadsheet Approach to Information Visualization. In: Robertson, George G. and Schmandt, Chris (eds.) Proceedings of the 10th annual ACM symposium on User interface software and technology October 14 - 17, 1997, Banff, Alberta, Canada. pp. 79-80. Available online
In information visualization, as the volume and complexity of the data increases, researchers require more powerful visualization tools that allow them to more effectively explore multi-dimensional datasets. In this paper, we show a novel new visualization framework built upon the spreadsheet metaphor, where each cell can contain an entire dataset. Just as a numerical spreadsheet enables exploration of numbers, a visualization spreadsheet enables exploration of visualizations of data. Our prototype spreadsheets enabled users to compare visualizations in cells using the tabular layout. Users can use the spreadsheet to display, manipulate, and explore multiple visual representation techniques for their data. By applying different operations to the cells, we showed how visualization spreadsheets afford the construction of 'what-if' scenarios. The possible set of operations that users can apply consists of animation, filtering, and algebraic operators.
© All rights reserved Chi et al. and/or ACM Press
Chi, Ed H., Barry, Phillip, Riedl, John and Konstan, Joseph A. (1997): A spreadsheet approach to information visualization. In: InfoVis 1997 - IEEE Symposium on Information Visualization October 18-25, 1997, Phoenix, AZ, USA. pp. 17-24. Available online
Konstan, Joseph A., Miller, Bradley N., Maltz, David, Herlocker, Jonathan L., Gordon, Lee R. and Riedl, John (1997): GroupLens: Applying Collaborative Filtering to Usenet News. In Communications of the ACM, 40 (3) pp. 77-87.
Herlocker, Jonathan L. and Konstan, Joseph A. (1995): Commands as Media: Design and Implementation of a Command Stream. In: ACM Multimedia 1995 1995. pp. 155-165. Available online
Rowe, Lawrence A., Konstan, Joseph A., Smith, Brian C., Seitz, Steve and Liu, Chung (1991): The PICASSO Application Framework. In: Rhyne, James R. (ed.) Proceedings of the 4th annual ACM symposium on User interface software and technology Hilton Head, South Carolina, United States, 1991, Hilton Head, South Carolina, United States. pp. 95-105. Available online
PICASSO is a graphical user interface development system that includes an interface toolkit and an application framework. The application framework provides high-level abstractions including modal dialog boxes and non-modal frames and panels similar to conventional programming language procedures and co-routines. These abstractions can be used to define objects that have local variables and that can be called with parameters. PICASSO also has a constraint system that is used to bind program variables to widgets, to implement triggered behaviors, and to implement multiple views of data. The system is implemented in Common Lisp using the Common Lisp Object System and the CLX interface to the X Window System.
© All rights reserved Rowe et al. and/or ACM Press
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