Publication statistics

Pub. period:2006-2012
Pub. count:14
Number of co-authors:30



Co-authors

Number of publications with 3 favourite co-authors:

Loren Terveen:
Shyong (Tony) K. Lam:
David Pitchford:

 

 

Productive colleagues

Shilad Sen's 3 most productive colleagues in number of publications:

Loren Terveen:69
Michael J. Muller:65
John Riedl:61
 
 
 

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Shilad Sen

 

Publications by Shilad Sen (bibliography)

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2012
 
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Priedhorsky, Reid, Pitchford, David, Sen, Shilad and Terveen, Loren (2012): Recommending routes in the context of bicycling: algorithms, evaluation, and the value of personalization. In: Proceedings of ACM CSCW12 Conference on Computer-Supported Cooperative Work 2012. pp. 979-988. Available online

Users have come to rely on automated route finding services for driving, public transit, walking, and bicycling. Current state of the art route finding algorithms typically rely on objective factors like time and distance; they do not consider subjective preferences that also influence route quality. This paper addresses that need. We introduce a new framework for evaluating edge rating prediction techniques in transportation networks and use it to explore ten families of prediction algorithms in Cyclopath, a geographic wiki that provides route finding services for bicyclists. Overall, we find that personalized algorithms predict more accurately than non-personalized ones, and we identify two algorithms with low error and excellent coverage, one of which is simple enough to be implemented in thin clients like web browsers. These results suggest that routing systems can generate better routes by collecting and analyzing users' subjective preferences.

© All rights reserved Priedhorsky et al. and/or ACM Press

 
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Kluver, Daniel, Nguyen, Tien T., Ekstrand, Michael, Sen, Shilad and Riedl, John (2012): How many bits per rating?. In: Proceedings of the 2012 ACM Conference on Recommender Systems 2012. pp. 99-106. Available online

Most recommender systems assume user ratings accurately represent user preferences. However, prior research shows that user ratings are imperfect and noisy. Moreover, this noise limits the measurable predictive power of any recommender system. We propose an information theoretic framework for quantifying the preference information contained in ratings and predictions. We computationally explore the properties of our model and apply our framework to estimate the efficiency of different rating scales for real world datasets. We then estimate how the amount of information predictions give to users is related to the scale ratings are collected on. Our findings suggest a tradeoff in rating scale granularity: while previous research indicates that coarse scales (such as thumbs up / thumbs down) take less time, we find that ratings with these scales provide less predictive value to users. We introduce a new measure, preference bits per second, to quantitatively reconcile this tradeoff.

© All rights reserved Kluver et al. and/or ACM Press

2011
 
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Lam, Shyong (Tony) K., Uduwage, Anuradha, Dong, Zhenhua, Sen, Shilad, Musicant, David R., Terveen, Loren and Riedl, John (2011): WP:clubhouse?: an exploration of Wikipedia's gender imbalance. In: Proceedings of the 2011 International Symposium on Wikis and Open Collaboration 2011. pp. 1-10. Available online

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 Wikipedia's legion of volunteer editors are female. In the current work, we present a scientific exploration of the gender imbalance in the English Wikipedia's 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 Wikipedia's articles. Further, we find evidence hinting at a culture that may be resistant to female participation.

© All rights reserved Lam et al. and/or ACM Press

 
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Sparling, E. Isaac and Sen, Shilad (2011): Rating: how difficult is it?. In: Proceedings of the 2011 ACM Conference on Recommender Systems 2011. pp. 149-156. Available online

Netflix.com uses star ratings, Digg.com uses up/down votes and Facebook uses a "like" but not a "dislike" button. Despite the popularity and diversity of these rating scales, research offers little guidance for designers choosing between them. This paper compares four different rating scales: unary ("like it"), binary (thumbs up / thumbs down), five-star, and a 100-point slider. Our analysis draws upon 12,847 movie and product review ratings collected from 348 users through an online survey. We a) measure the time and cognitive load required by each scale, b) study how rating time varies with the rating value assigned by a user, and c) survey users' satisfaction with each scale. Overall, users work harder with more granular rating scales, but these effects are moderated by item domain (product reviews or movies). Given a particular scale, users rating times vary significantly for items they like and dislike. Our findings about users' rating effort and satisfaction suggest guidelines for designers choosing between rating scales.

© All rights reserved Sparling and Sen and/or ACM Press

2010
 
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Vig, Jesse, Soukup, Matthew, Sen, Shilad and Riedl, John (2010): Tag expression: tagging with feeling. In: Proceedings of the 2010 ACM Symposium on User Interface Software and Technology 2010. pp. 323-332. Available online

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 community's 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 users' motivations in tagging and measures user satisfaction with tag expression.

© All rights reserved Vig et al. and/or their publisher

2009
 
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Vig, Jesse, Sen, Shilad and Riedl, John (2009): Tagsplanations: explaining recommendations using tags. In: Proceedings of the 2009 International Conference on Intelligent User Interfaces 2009. pp. 47-56. Available online

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.

© All rights reserved Vig et al. and/or their publisher

 
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Sen, Shilad, Vig, Jesse and Riedl, John (2009): Learning to recognize valuable tags. In: Proceedings of the 2009 International Conference on Intelligent User Interfaces 2009. pp. 87-96. Available online

Many websites use tags as a mechanism for improving item metadata through collective user effort. 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 offline analyses of 21 tag selection algorithms. We select the three best performing algorithms from our offline analysis, and deploy them live on the MovieLens website to 5,695 users for three months. Based on our results, we offer tagging system designers advice about tag selection algorithms.

© All rights reserved Sen et al. and/or their publisher

 
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Sen, Shilad, Vig, Jesse and Riedl, John (2009): Tagommenders: connecting users to items through tags. In: Proceedings of the 2009 International Conference on the World Wide Web 2009. pp. 671-680. Available online

Tagging has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. Recommender systems similarly enable users to efficiently navigate vast collections of items. Algorithms combining tags with recommenders may deliver both the automation inherent in recommenders, and the flexibility and conceptual comprehensibility inherent in tagging systems. In this paper we explore tagommenders, recommender algorithms that predict users' preferences for items based on their inferred preferences for tags. We describe tag preference inference algorithms based on users' 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 users' 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 flexible recommender systems that leverage the characteristics of items users find most important.

© All rights reserved Sen et al. and/or ACM Press

2008
 
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Drenner, Sara, Sen, Shilad and Terveen, Loren (2008): Crafting the initial user experience to achieve community goals. In: Proceedings of the 2008 ACM Conference on Recommender Systems 2008. pp. 187-194. Available online

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.

© All rights reserved Drenner et al. and/or ACM Press

2007
 
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Sen, Shilad, Harper, F. Maxwell, LaPitz, Adam and Riedl, John (2007): The quest for quality tags. In: GROUP07: International Conference on Supporting Group Work 2007. pp. 361-370. Available online

Many online communities use tags -- community selected words or phrases -- 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 individual's 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.

© All rights reserved Sen et al. and/or ACM Press

 
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Frankowski, Dan, Lam, Shyong K., Sen, Shilad, Harper, F. Maxwell, Yilek, Scott, Cassano, Michael and Riedl, John (2007): Recommenders everywhere: the WikiLens community-maintained recommender system. In: Proceedings of the 2007 International Symposium on Wikis 2007. pp. 47-60. Available online

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? WikiLens is open source software that enables anyone, anywhere to start a community-maintained recommender 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 small world, 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.

© All rights reserved Frankowski et al. and/or ACM Press

 
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Harper, F. Maxwell, Sen, Shilad and Frankowski, Dan (2007): Supporting social recommendations with activity-balanced clustering. In: Proceedings of the 2007 ACM Conference on Recommender Systems 2007. pp. 165-168. Available online

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.

© All rights reserved Harper et al. and/or ACM Press

2006
 
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Sen, Shilad, Geyer, Werner, Muller, Michael J., Moore, Marty, Brownholtz, Beth, Wilcox, Eric and Millen, David R. (2006): FeedMe: a collaborative alert filtering system. In: Proceedings of ACM CSCW06 Conference on Computer-Supported Cooperative Work 2006. pp. 89-98. Available online

As the number of alerts generated by collaborative applications grows, users receive more unwanted alerts. FeedMe is a general alert management system based on XML feed protocols such as RSS and ATOM. In addition to traditional rule-based alert filtering, FeedMe uses techniques from machine-learning to infer alert preferences based on user feedback. In this paper, we present and evaluate a new collaborative naive Bayes filtering algorithm. Using FeedMe, we collected alert ratings from 33 users over 29 days. We used the data to design and verify the accuracy of the filtering algorithm and provide insights into alert prediction.

© All rights reserved Sen et al. and/or ACM Press

 
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Sen, Shilad, Lam, Shyong K., Rashid, Al Mamunur, Cosley, Dan, Frankowski, Dan, Osterhouse, Jeremy, Harper, F. Maxwell and Riedl, John (2006): tagging, communities, vocabulary, evolution. In: Proceedings of ACM CSCW06 Conference on Computer-Supported Cooperative Work 2006. pp. 181-190. Available online

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.

© All rights reserved Sen et al. and/or ACM Press

 
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