An online evaluation of explicit feedback mechanisms for recommender systems

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Poster about an online feedback experiment as presented during the WEBIST 2011 conference in Noordwijkerhout (The Netherlands), May 7, 2011 by Simon Dooms.

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An Online Evaluation of Explicit Feedback
Mechanisms for Recommender Systems
Simon Dooms
simon.dooms@intec.ugent.be
Toon De Pessemier
toon.depessemier@intec.ugent.be
Luc Martens
Luc.martens@intec.ugent.be
Explicit Feedback Anonymous Vs User Cumulative number of Explicit Ratings
WiCa, Wireless & Cable, www.wica.intec.ugent.be
Gaston Crommenlaan 8 box 201, 9050 Ghent, Belgium
Ghent University, Department of Information Technology
1 Introduction
Recommender algorithms need feedback data
Feedback systems collect this input from the user
Can be explicit (ratings) or implicit (logs in the background)
Ratings can be dynamic (JavaScript) or static (HTML forms)
2 The Experiment
On the pages of a popular Belgian cultural website we
randomly put one of the four most used rating mechanisms
(dynamic thumbs, dynamic stars, static thumbs and
static stars) and logged the interaction of the users. Next to
this explicit feedback, we tracked also implicit feedback in
the form of click and browse behavior.
3 The Results
6 months of data
8 100 explicit ratings
200 000 implicit ratings
800 000 pageviews
5-Star
(dynamic)
Thumbs
(static)
Thumbs
(dynamic)
5-Star
(static)
1330 1694 2101 2976
16 % 21 % 26 % 37%
4 Conclusion
Users don’t prefer dynamic over static systems
Most (explicit) ratings are provided anonymously
The 5-stars systems are used as thumbs systems
(The extreme star values i.e. 1 and 5 are most commonly used)
Explicit feedback is hard to get, implicit feedback is easy
The static 5-stars rating system collected the most feedback
5 Future Work
Optimize feedback collection
How can users be encouraged to rate?
How to combine explicit and implicit feedback?
What feedback system collects most valuable feedback?
Quantify the relevancy of explicit versus implicit feedback
Extract events dataset for future research
Rating Values Distribution for Thumbs and Stars
Explicit Ratings per day
Implicit Ratings per day

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An online evaluation of explicit feedback mechanisms for recommender systems

1. An Online Evaluation of Explicit Feedback Mechanisms for Recommender Systems Simon Dooms simon.dooms@intec.ugent.be Toon De Pessemier toon.depessemier@intec.ugent.be Luc Martens Luc.martens@intec.ugent.be Explicit Feedback Anonymous Vs User Cumulative number of Explicit Ratings WiCa, Wireless & Cable, www.wica.intec.ugent.be Gaston Crommenlaan 8 box 201, 9050 Ghent, Belgium Ghent University, Department of Information Technology 1 Introduction Recommender algorithms need feedback data Feedback systems collect this input from the user Can be explicit (ratings) or implicit (logs in the background) Ratings can be dynamic (JavaScript) or static (HTML forms) 2 The Experiment On the pages of a popular Belgian cultural website we randomly put one of the four most used rating mechanisms (dynamic thumbs, dynamic stars, static thumbs and static stars) and logged the interaction of the users. Next to this explicit feedback, we tracked also implicit feedback in the form of click and browse behavior. 3 The Results 6 months of data 8 100 explicit ratings 200 000 implicit ratings 800 000 pageviews 5-Star (dynamic) Thumbs (static) Thumbs (dynamic) 5-Star (static) 1330 1694 2101 2976 16 % 21 % 26 % 37% 4 Conclusion Users don’t prefer dynamic over static systems Most (explicit) ratings are provided anonymously The 5-stars systems are used as thumbs systems (The extreme star values i.e. 1 and 5 are most commonly used) Explicit feedback is hard to get, implicit feedback is easy The static 5-stars rating system collected the most feedback 5 Future Work Optimize feedback collection How can users be encouraged to rate? How to combine explicit and implicit feedback? What feedback system collects most valuable feedback? Quantify the relevancy of explicit versus implicit feedback Extract events dataset for future research Rating Values Distribution for Thumbs and Stars Explicit Ratings per day Implicit Ratings per day

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