About Contact Help us

Join us ! Publish !

Interaction Design Foundation

Free educational materials - made by the world's technology elite

About sponsorships

References

Jesse Vig

iPad and tablet version

Publication statistics

Pub. period:2009-2010
Pub. count:5
Number of co-authors:3

Co-authors

Number of publications with 3 favourite co-authors:

Shilad Sen:4
John Riedl:4
Matthew Soukup:1

Productive colleagues

Jesse Vig's 3 most productive colleagues in number of publications:

John Riedl:61
Shilad Sen:14
Matthew Soukup:1

Calendar

May 27

13th Intl. Conf. on New Interfaces for Musical Expression

May 28

Graphics Interface 2013

May 29

Boston UXPA Usability and UX Conf. 2013

May 24

“ For a list of all the ways technology has failed to improve the quality of life, please press three. ”

-- Alice Kahn

Share this quote on...
Get more quotes via RSS or by joining our Usability Quote of the Day Facebook Page

Featured chapter

Read the fascinating history of Wearable Computing, told by its father, Steve Mann

Read Steve's chapter !

Help us help you!

Spread the word:
Donate
Other ways to help

Jesse Vig

Add description

← Button will not work: You have not enabled Javascript. How do I enable it?

Rename / change spelling

Add publication

← Button will not work: You have not enabled Javascript. How do I enable it?

Publications by Jesse Vig (bibliography)

2010

Edit | Del

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.

“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.”

Edit | Del

Vig, Jesse (2010): Intelligent tagging interfaces: beyond folksonomy. In: Proceedings of the 2010 ACM Symposium on User Interface Software and Technology 2010. pp. 371-374.

“This paper summarizes our work on using tags to broaden the dialog between a recommender system and its users. We present two tagging applications that enrich this dialog: tagsplanations are tag-based explanations of recommendations provided by a system to its users, and Movie Tuner is a conversational recommender system that enables users to provide feedback on movie recommendations using tags. We discuss the design of both systems and the experimental methodology used to evaluate the design choices.”

2009

Edit | Del

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.

“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.”

Edit | Del

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.

“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.”

Edit | Del

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.

“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.”

Add publication