Diversity in recommender systems: sketch of a bibliography

I have been arguing on this blog that while everyone knows diversity is a desirable property of recommender systems, there has been little work on the topic. To make my claim precise, I decided to list the papers addressing both recommender systems and diversity. I mean this list to be complete.

You can find a few more references and some analysis in our technical report:

Daniel Lemire, Stephen Downes, Sébastien Paquet, Diversity in open social networks, published online, October 2008.

If I am missing any paper, tell me!

Maybe this warrants a Wikipedia page?

7 thoughts on “Diversity in recommender systems: sketch of a bibliography”

  1. Very interesting topic Daniel!

    I see two approaches regarding ‘diversity’ in RS:

    1) Try to avoid too similar items in the top-N recommended items.

    E.g Avoid this: given a user that loves Woody Allen, then recommend Woody Allen films that are not in her profile.

    Also know as “The White Album effect”:
    http://www.amazon.com/Beatles-White-Album/dp/B000002UAX

    2) Try to present a mix of familiar and unknown items to the user.

    E.g. Given a user that really likes The Rolling Stones, recommend to her:
    The Beatles
    The Who
    Mick Jagger
    Led Zeppelin
    etc.

    Most of these recommendations are obvious, so why do I need a recommender, then?

    However, it would be great to get some more “interesting” recommendations, such as:
    Robert Johnson
    Muddy Waters
    Chuck Berry
    The Faces
    The Georgia Satellites
    Sol Lagarto
    Diamond Dogs
    The Dogs d’Amour

    I’m particularly interested in the second approach, as it really helps users discover unknown items that otherwise would have been very difficult to get into.

    Cheers, Oscar

  2. Some other references for diversity in recommendation:

    Measuring playlist diversity for recommendation systems by Malcolm Slaney and William White
    Oscar’s (http://mtg.upf.edu/~ocelma) thesis (when he finally publishes it
    Beyond Algorithms: An HCI Perspective on Recommender Systems
    Kirsten Swearingen & Rashmi Sinha
    Evaluating Collaborative Filtering
    Recommender Systems – Herlocker, Konstan, Terveen, Riedl

  3. Here are some papers which I also consider relevant:

    * Zhai, Cohen, Lafferty: “Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval” SIGIR 03

    * Song, Tian, Huang: “Improving the Image Retrieval Result Via Topic Coverage Grap” PCM2006

    * Chen, Karger: “Less is More: Probabilistic Models for Retrieving Fewer Relevant Documents” SIGIR06

    * Tang, Arni, Sanderson, Clough: “Building a Diversity Featured Search System by Fusing Existing Tools”

    * Arni, Clough, Sanderson, Grubinger: “Overview of the ImageCLEF 2008 Photographic Retrieval Task”

    * Clarke, Kolla, Cormack, Vechtomova, Ashkan, Büttcher, MacKinnon: “Novelty and Diversity in Information Retrieval Evaluation” SIGIR08

    * Xu, Yin: “Novelty and Topicality in Interactive Information Retrieval”

    You might also be interested in the photo retrieval task of ImageCLEF 2008, where diversity was taken into account.

  4. Wow, Beyond Algorithms is great!

    Fig 7 is particularly interesting in the debate about whether CF will narrow our interests.

    It also shows the importance of transparency (Table 1 + Fig 10).

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