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.
- L. McGinty, B. Smyth, On the Role of Diversity in Conversational Recommender Systems, in: Proc. ICCBR 2003, 2003.
- Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, Georg Lausen, Improving Recommendation Lists Through Topic Diversification, Proceedings of the 14th International World Wide Web Conference (WWW ’05), May 10-14, 2005, Chiba, Japan. (Thanks to Daniel Haran for pointing me this one.)
- D. Fleder, K. Hosanagar, Blockbuster culture’s next rise or fall: The effect of recommender systems on sales diversity, in: Proc. WISE 2006, 2006.
- S. M. McNee, J. Riedl, J. A. Konstan, Being accurate is not enough: how accuracy metrics have hurt recommender systems, in: Proc. CHI ’06 (2006) 1097 – 1101.
- Zhang, M. and Hurley, N. 2008. Avoiding monotony: improving the diversity of recommendation lists. In Proceedings of the 2008 ACM Conference on Recommender Systems (Lausanne, Switzerland, October 23 – 25, 2008). RecSys ’08. ACM, New York, NY, 123-130.
- Quoc Le, Alexander Smola, Direct Optimization of Ranking Measures, published online, 2008. (Thanks Mark Reid.)
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?
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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.
Comment by Thomas Deselaers — 24/11/2008 @ 10:58
Isn’t lack of diversity some sort of overfitting?
If not how you would you define the difference?
Comment by Kevembuangga — 24/11/2008 @ 11:08
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
Comment by Oscar — 24/11/2008 @ 12:44
Oscar: Good point. And this stresses the fact that “probabilistically less accurate recommendations” may prove far more valuable.
Comment by Daniel Lemire — 24/11/2008 @ 13:38
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
Comment by Paul — 24/11/2008 @ 14:32
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).
Comment by Daniel Haran — 26/11/2008 @ 11:27
I would add this to your list of papers Daniel:
Recommender systems and their impact on sales diversity
http://portal.acm.org/citation.cfm?id=1250910.1250939
http://doi.acm.org/10.1145/1250910.1250939
Comment by Andre Vellino — 26/11/2008 @ 15:32