3 Shyong K. Lam Anuradha Uduwage Zhenhua Dong Shilad Sen David R. Musicant Loren Terveen John Riedl 2011 WP:Clubhouse? An Exploration of Wikipedia’s Gender Imbalance WikiSym 2011 Mountain View, CA ACM 10/2011 Wikipedia, collaboration, gender gap, content coverage Wikipedia has rapidly become an invaluable destination for millions of information-seeking users. However, media reports suggest an important challenge: only a small fraction of Wikipediaâs legion of volunteer editors are female. In the current work, we present a scientific exploration of the gender imbalance in the English Wikipediaâs population of editors. We look at the nature of the imbalance itself, its effects on the quality of the encyclopedia, and several conflict-related factors that may be contributing to the gender gap. Our findings confirm the presence of a large gender gap among editors and a corresponding gender-oriented disparity in the content of Wikipediaâs articles. Further, we find evidence hinting at a culture that may be resistant to female participation. http://grouplens.org/system/files/wp-gender-wikisym2011.pdf 3 Vig, J. Sen, S. Riedl, J. 2011 Navigating the Tag Genome International Conference on Intelligent User Interfaces Palo Alto, CA Tags help users understand a rich information space, by showing them specific text annotations for each item in the space and enabling them to search by these annotations. Often, however, users may wish to move from one item to other items that are similar overall, but that differ in key characteristics. For example, a user who loves Pulp Fiction might want to see a similar movie, but might be in a mood for a less âdarkâ movie. This paper introduces Movie Tuner, a novel interface that supports navigation from one item to nearby items along dimensions represented by tags. Movie Tuner is based on a data structure called the tag genome, which is described in separate work. The tag genome encodes each itemâs relationship to a common set of tags by applying machine learning algorithms to user-contributed content. The present paper discusses our design of Movie Tuner, including algorithms for navigating to new items and for suggesting tags for navigation. We present the results of a 7-week field trial of 2,531 users of Movie Tuner, and of a survey evaluating users subjective experience. 3 Vig, J. Soukup, M. Sen, S. Riedl, J. 2010 Tag expression: tagging with feeling ACM Symposium on User Interface and Technology New York, NY Association for Computing Machinery 10/2010 community, ratings, tagging, user preference In this paper we introduce tag expression, a novel form of preference elicitation that combines elements from tagging and rating systems. Tag expression enables users to apply affect to tags to indicate whether the tag describes a reason they like, dislike, or are neutral about a particular item. We present a user interface for applying affect to tags, as well as a technique for visualizing the overall communityâs affect. By analyzing 27,773 tag expressions from 553 users entered in a 3-month period, we empirically evaluate our design choices. We also present results of a survey of 97 users that explores usersâ motivations in tagging and measures user satisfaction with tag expression. 10 Jesse Vig Shilad Sen John Riedl 2010 Computing the Tag Genome Technical Report Minneapolis University of Minnesota 3 Sen, S. Vig, J. Riedl, J. 2009 Tagommenders: Connecting Users to Items through Tags International World Wide Web Conference Madrid, Spain ACM 4/20/2009 <p>Tagging has emerged as a powerful mechanism that enables users to ï¬nd, organize, and understand online entities. Recommender systems similarly enable users to efï¬ciently navigate vast collections of items. Algorithms combining tags with recommenders may deliver both the automation inherent in recommenders, and the ï¬exibility and conceptual comprehensibility inherent in tagging<br />systems. In this paper we explore tagommenders, recommender algorithms that predict usersâ preferences for items based on their inferred preferences for tags. We describe tag preference inference algorithms based on usersâ interactions with tags and movies, and evaluate these algorithms based on tag preference ratings collected from 995 MovieLens users. We design and evaluate algorithms that predict usersâ ratings for movies based on their inferred tag preferences. Our tag-based algorithms generate better recommendation rankings than state-of-the-art algorithms, and they may lead to ï¬exible recommender systems that leverage the characteristics of items users ï¬nd most important.</p> 3 Sen, S. Vig, J. Riedl, J. 2009 Learning to Recognize Valuable Tags International Conference on Intelligent User Interfaces Sanibel Island, FL ACM 2/8/2009 <p>Many websites use tags as a mechanism for improving item metadata through collective user eï¬ort. Users of tagging systems often apply far more tags to an item than a system can display. These tags can range in quality from tags that capture a key facet of an item, to those that are subjective, irrelevant, or misleading. In this paper we explore tag selection algorithms that choose the tags that sites display. Based on 225,000 ratings and survey responses, we conduct oï¬ine analyses of 21 tag selection algorithms. We select the three best performing algorithms from our oï¬ine analysis, and deploy them live on the MovieLens website to 5,695 users for three months. Based on our results, we oï¬er tagging system designers advice about tag selection algorithms.</p> 3 Vig, J. Sen, S. Riedl, J. 2009 Tagsplanations: Explaining Recommendations using Tags International Conference on Intelligent User Interfaces Sanibel Island, FL 02/08/2009 tagging, recommender systems <p>While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many beneï¬ts, 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 userâs 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.</p> 3 Drenner, S. Sen, S. Terveen, L. 2008 Crafting the initial user experience to achieve community goals ACM Conference on recommender Systems Lausanne, Switzerland ACM 978-1-60558-093-7 <p>Recommender systems try to address the "new user problem" by quickly and painlessly learning user preferences so that users can begin receiving recommendations as soon as possible. We take an expanded perspective on the new user experience, seeing it as an opportunity to elicit valuable contributions to the community and shape subsequent user behavior. We conducted a field experiment in MovieLens where we imposed additional work on new users: not only did they have to rate movies, they also had to enter varying numbers of tags. While requiring more work led to fewer users completing the entry process, the benefits were significant: the remaining users produced a large volume of tags initially, and continued to enter tags at a much higher rate than a control group. Further, their rating behavior was not depressed. Our results suggest that careful design of the initial user experience can lead to significant benefits for an online community.</p> http://portal.acm.org/citation.cfm?doid=1454008.1454039 3 F. Maxwell Harper Shilad Sen Dan Frankowski 2007 Supporting social recommendations with activity-balanced clustering ACM Conference On Recommender Systems Minneapolis, MN, USA ACM 19/10/2007 978-1-59593-730--8 In support of social interaction and information sharing, online communities commonly provide interfaces for users to form or interact with groups. For example, a user of the social music recommendation site last.fm might join the "First Wave Punk" group to discuss his or her favorite band (The Clash) and listen to playlists generated by fellow fans. Clustering techniques provide the potential to automatically discover groups of users who appear to share interests. We explore this idea by describing algorithms for clustering users of an online community and automatically describing the resulting user groups. We designed these techniques for use in an online recommendation system with no pre-existing group functionality, which led us to develop an "activity-balanced clustering" algorithm that considers both user activity and user interests in forming clusters. http://doi.acm.org/10.1145/1297231.1297262 3 Dan Frankowski Shyong K. Lam Shilad Sen F. Maxwell Harper Scott Yilek Michael Cassano John Riedl 2007 Recommenders Everywhere: The WikiLens Community-Maintained Recommender System Wikisym 2007 Montreal, Quebec, Canada 978-1-59593-861-9 <p>Suppose you have a passion for items of a certain type, and you wish to start a recommender system around those items. You want a system like Amazon or Epinions, but for cookie recipes, local theater, or microbrew beer. How can you set up your recommender system without assembling complicated algorithms, large software infrastructure, a large community of contributors, or even a full catalog of items?</p> <p>WikiLens is open source software that enables anyone, anywhere to start a <em>community-maintained recommender</em> around any type of item. We introduce five principles for community-maintained recommenders that address the two key issues: (1) community contribution of items and associated information; and (2) finding items of interest. Since all recommender communities start small, we look at feasibility and utility in the <em>small world</em>, one with few users, few items, few ratings. We describe the features of WikiLens, which are based on our principles, and give lessons learned from two years of experience running wikilens.org.</p> http://www-users.cs.umn.edu/~dfrankow/files/wiki06f-frankowski.pdf 3 Shilad Sen F. Maxwell Harper Adam LaPitz John Riedl 2007 The Quest for Quality Tags Conference on Supporting Group Work <p>Many online communities use tags â community selected words or phrases â to help people find what they desire. The quality of tags varies widely, from tags that capture a key dimension of an entity to those that are profane, useless, or unintelligible. Tagging systems must often select a subset of available tags to display to users due to limited screen space. Because users often spread tags they have seen, selecting good tags not only improves an individualâs view of tags, it also encourages them to create better tags in the future. We explore implicit (behavioral) and explicit (rating) mechanisms for determining tag quality. Based on 102,056 tag ratings and survey responses collected from 1,039 users over 100 days, we offer simple suggestions to designers of online communities to improve the quality of tags seen by their users.</p> http://www.grouplens.org/system/files/group07-sen.pdf 3 Dan Frankowski Dan Cosley Shilad Sen Loren Terveen John Riedl 2006 You Are What You Say: Privacy Risks of Public Mentions Annual ACM Conference on Research and Development in Information Retrieval Seattle Washington ACM 565-572 06/08/2006 1-59593-369-7 <p>In today's data-rich networked world, people express many aspects of their lives online. It is common to segregate different aspects in different places: you might write opinionated rants about movies in your blog under a pseudonym while participating in a forum or web site for scholarly discussion of medical ethics under your real name. However, it may be possible to link these separate identities, because the movies, journal articles, or authors you mention are from a sparse relation space whose properties (e.g., many items related to by only a few users) allow re-identification. This re-identification violates people's intentions to separate aspects of their life and can have negative consequences; it also may allow other privacy violations, such as obtaining a stronger identifier like name and address.This paper examines this general problem in a specific setting: re-identification of users from a public web movie forum in a private movie ratings dataset. We present three major results. First, we develop algorithms that can re-identify a large proportion of public users in a sparse relation space. Second, we evaluate whether private dataset owners can protect user privacy by hiding data; we show that this requires extensive and undesirable changes to the dataset, making it impractical. Third, we evaluate two methods for users in a public forum to protect their own privacy, suppression and misdirection. Suppression doesn't work here either. However, we show that a simple misdirection strategy works well: mention a few popular items that you haven't rated.</p>

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 3 Shilad Sen Shyong K. Lam Dan Cosley Al Mamunur Rashid Dan Frankowski Franklin Harper Jeremy Osterhouse John Riedl 2006 tagging, community, vocabulary, evolution Computer Supported Cooperative Work Banff, Alberta, Canada ACM 181-190 04/11/2006 1-59593-249-6 <p>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.</p> http://www.grouplens.org/papers/pdf/sen-cscw2006.pdf