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06 Aug 08

Flickr tag recommendation based on collective knowledge

  • Online photo services such as Flickr and Zooomr allow users to share their photos with family, friends, and the online community at large. An important facet of these services is that users manually annotate their photos using so called tags, which describe the contents of the photo or provide additional contextual and semantical information. In this paper we investigate how we can assist users in the tagging phase. The contribution of our research is twofold. We analyse a representative snapshot of Flickr and present the results by means of a tag characterisation focussing on how users tags photos and what information is contained in the tagging. Based on this analysis, we present and evaluate tag recommendation strategies to support the user in the photo annotation task by recommending a set of tags that can be added to the photo. The results of the empirical evaluation show that we can
  • Online photo services such as Flickr and Zooomr allow users to share their photos with family, friends, and the online community at large. An important facet of these services is that users manually annotate their photos using so called tags, which describe the contents of the photo or provide additional contextual and semantical information. In this paper we investigate how we can assist users in the tagging phase. The contribution of our research is twofold. We analyse a representative snapshot of Flickr and present the results by means of a tag characterisation focussing on how users tags photos and what information is contained in the tagging. Based on this analysis, we present and evaluate tag recommendation strategies to support the user in the photo annotation task by recommending a set of tags that can be added to the photo. The results of the empirical evaluation show that we can
27 Jul 08

The REFER Home Page

  • REFER is a research group in the Computer Science department at WPI. The members of REFER share interests in issues relevant to the design and analysis of personalized information systems that recommend items of potential interest to their users on the basis of descriptions of these items ("content") as well as social ("collaborative") information about the relations between different users' tastes.
26 Jul 08

Getting our head in the clouds

Rivadeneira, A. W., Gruen, D. M., Muller, M. J., and Millen, D. R. 2007. Getting our head in the clouds: toward evaluation studies of tagclouds. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (San Jose, California, USA, Apri

portal.acm.org/citation.cfm - Preview

HMDB folksonomy recommendation visualisation

  • agclouds are visual presentations of a set of words, typically a set of "tags" selected by some rationale, in which attributes of the text such as size, weight, or color are used to represent features, such as frequency, of the associated terms. This note describes two studies to evaluate the effectiveness of differently constructed tagclouds for the various tasks they can be used to support, including searching, browsing, impression formation and recognition. Based on these studies, we propose a paradigm for evaluating tagclouds and ultimately guidelines for tagcloud construction.

Learning user profiles from tagging data and leveraging them

  • Michlmayr, E., Cayzer, S.: Learning user profiles from tagging data and leveraging them
    for personal(ized) information access. In 16th International World Wide Web Conference
    (WWW2007), Banff, Canada (2007)

Millen: using social tagging to imporve - Google Search

  • Millen, D.R., Feinberg, J.: Using social tagging to improve social navigation. In Workshop
    on the Social Navigation and Community Based Adaptation Technologies. Dublin, Ireland
    (2006)

Erickson Social translucence: using minimalist - Google Search

  • Erickson, T. and Kellogg, W. A. 2003. Social translucence: using minimalist visualisations of social activity to support collective interaction. In Designing information Spaces: the Social Navigation Approach, K. Höök, D. Benyon, A. J. Munro, D. Diaper, and C. Sanger, Eds. Springer-Verlag, London, 17-41.

Implicit Interest Indicators

  • Mark Claypool, Phong Le, Makoto Waseda and David Brown
    In Proceedings of ACM Intelligent User Interfaces Conference (IUI)
    Santa Fe, New Mexico, USA
    January 14-17, 2001
    Winner! Best paper award.
14 Apr 08

Bookmark Hierarchies and Collaborative Recommendation

interesting, although using the hierarchies of bookmarks rather than flat structures.

www.informatics.indiana.edu/...GAL06.pdf - Preview

social networks recommendation social_bookmarking

25 Feb 08

Rethinking Recommendation Engines - ReadWriteWeb

  • Over two years ago, Netflix announced a Recommendation Engine contest - anyone who invents an algorithm that does 10% better than their current recommendation system will win $1 Million dollars. Many research teams raced to attack the problem, excited by the unprecedented amount of data available. Initially quite a lot of progress was made, but then slowly the progress stalled and now teams are stuck at around the 8.5% improvement mark.
13 Feb 08

Is the Tipping Point Toast? -- Duncan Watts -- Trendsetting | Fast Company

  • The technique marries Watts's two main epiphanies: Cascades require word-of-mouth effects, so you need to build a six-degrees effect into an ad campaign; but since you can never know which person is going to spark the fire, you should aim the ad at as broad a market as possible--and not waste money chasing "important" people. And it worked. The pass-around effect doubled the number of people who saw the Brady Campaign's ad. They paid for 22,582 hits and received an additional 31,590 for free. Another campaign they ran for the Oxygen network quadrupled the audience size, adding 23,544 hits to the initial 7,064.
15 Jan 08

Tagomendations - making recommendations transparent - Duke Listens!

Great demonstration by P.Lamere demonstrating how to use tags for explainable recommendations. Love it!

blogs.sun.com/...ing_recommedations_transparent - Preview

folksonomy read_write_web recommendation

  • That's it in a nutshell - how we are using social tags to generate transparent,� explainable� recommendations.�� And by the way, to do the heavy lifting for our Tagomendations� we are using the text search engine called Minion, developed by the Advanced Search Technology group here at Sun Labs.� Minion is a high quality, highly configurable search engine that is perfect for doing these types of experiments.� Look for Minion, It's coming to an open source repository near you very soon.
16 Sep 07

Hybrid Web Recommender Systems

  • Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have combined recommendation techniques to build hybrid recommender systems. This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies. Implementations of 41 hybrids including some novel combinations are examined and compared. The study finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths.

Recommendation to Groups

  • Recommender systems have traditionally recommended items to individual users, but there has recently been a proliferation of recommenders that address their recommendations to groups of users. The shift of focus from an individual to a group makes more of a difference than one might at first expect. This chapter discusses the most important new issues that arise, organizing them in terms of four subtasks that can or must be dealt with by a group recommender: 1. acquiring information about the user’s preferences; 2. generating recommendations; 3. explaining recommendations; and 4. helping users to settle on a final decision. For each issue, we discuss how it has been dealt with in existing group recommender systems and what open questions call for further research.
09 Aug 07

Recommender Systems, a university course by Resnik

  • Recommender systems guide people to interesting materials based on information from other people. There is a large design space of alternative ways to organize such systems. The information that other people provide may come from explicit ratings, tags, or reviews, or implicitly from how they spend their time or money. The information can be aggregated and used to select, filter, sort, or highlight items. The recommendations may be personalized to the preferences of different users.

ACM Recommender Systems 2007

  • The following five student submissions were selected to participate in the RecSys 2007 doctoral symposium. While the symposium itself is a private event for these students and their faculty mentors, their work will be displayed at the poster and demo session on Friday, October 19th.
27 May 07

Understanding the Navigability of Social Tagging System (Chi & Mytkowicz)

  • Another paper submitted for the alt.chi 2007 conference. This comes from PARC and is a study of the del.icio.us website using metrics based on entropy.
25 May 07

understanding navigability of social tagging systems

  • Given the rise in popularity of social tagging systems, it
    seems only natural to ask how efficient is the organically
    evolved vocabulary in describing any underlying document
    objects? Does this distributed process really provide a way
    to circumnavigate the traditional categorization problem
    with ontologies? We analyze a social tagging site, namely
    del.icio.us, with information theory in order to evaluate the
    efficiency of this social tagging site for navigation to
    information sources. We show that over time, del.icio.us is
    becoming harder and harder to navigate and provide an
    evaluation metric, namely entropy, that can be used to
    evaluate and drive system design choices.
24 May 07

Four Recommendations Paradigms to Watch

  • What's the best logic model to use for a recommendations engine? Builders of popular music recommendations services shared the pros and cons of their own - and each other's - approaches to matching people to the music that they'll love. These four paradigms for recommendations engines came out of the conversation. Most recommendation services will fall into one of these models, but the 'best approach' would undoubtedly vary based upon the subject matter
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