We've updated our privacy policy. Click here to review the details. Tap here to review the details.
Activate your 30 day free trial to unlock unlimited reading.
Activate your 30 day free trial to continue reading.
Download to read offline
Correcting Popularity Bias by Enhancing Recommendation Neutrality on
The 8th ACM Conference on Recommender Systems, Poster
Article @ Official Site: http://ceur-ws.org/Vol-1247/
Article @ Personal Site: http://www.kamishima.net/archive/2014-po-recsys-print.pdf
Abstract:
In this paper, we attempt to correct a popularity bias, which is the tendency for popular items to be recommended more frequently, by enhancing recommendation neutrality. Recommendation neutrality involves excluding specified information from the prediction process of recommendation. This neutrality was formalized as the statistical independence between a recommendation result and the specified information, and we developed a recommendation algorithm that satisfies this independence constraint. We correct the popularity bias by enhancing neutrality with respect to information regarding whether candidate items are popular or not. We empirically show that a popularity bias in the predicted preference scores can be corrected.
Correcting Popularity Bias by Enhancing Recommendation Neutrality on
The 8th ACM Conference on Recommender Systems, Poster
Article @ Official Site: http://ceur-ws.org/Vol-1247/
Article @ Personal Site: http://www.kamishima.net/archive/2014-po-recsys-print.pdf
Abstract:
In this paper, we attempt to correct a popularity bias, which is the tendency for popular items to be recommended more frequently, by enhancing recommendation neutrality. Recommendation neutrality involves excluding specified information from the prediction process of recommendation. This neutrality was formalized as the statistical independence between a recommendation result and the specified information, and we developed a recommendation algorithm that satisfies this independence constraint. We correct the popularity bias by enhancing neutrality with respect to information regarding whether candidate items are popular or not. We empirically show that a popularity bias in the predicted preference scores can be corrected.
You just clipped your first slide!
Clipping is a handy way to collect important slides you want to go back to later. Now customize the name of a clipboard to store your clips.The SlideShare family just got bigger. Enjoy access to millions of ebooks, audiobooks, magazines, and more from Scribd.
Cancel anytime.Unlimited Reading
Learn faster and smarter from top experts
Unlimited Downloading
Download to take your learnings offline and on the go
You also get free access to Scribd!
Instant access to millions of ebooks, audiobooks, magazines, podcasts and more.
Read and listen offline with any device.
Free access to premium services like Tuneln, Mubi and more.
We’ve updated our privacy policy so that we are compliant with changing global privacy regulations and to provide you with insight into the limited ways in which we use your data.
You can read the details below. By accepting, you agree to the updated privacy policy.
Thank you!