Scheduled System Maintenance:
On Wednesday, July 29th, IEEE Xplore will undergo scheduled maintenance from 7:00-9:00 AM ET (11:00-13:00 UTC). During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Web-Scale Media Recommendation Systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Dror, G. ; Yahoo! Research Labs, Haifa, Israel ; Koenigstein, N. ; Koren, Y.

Modern consumers are inundated with choices. A variety of products are offered to consumers, who have unprecedented opportunities to select products that meet their needs. The opportunity for selection also presents a time-consuming need to select. This has led to the development of recommender systems that direct consumers to products expected to satisfy them. One area in which such systems are particularly useful is that of media products, such as movies, books, television, and music. We study the details of media recommendation by focusing on a large scale music recommender system. To this end, we introduce a music rating data set that is likely to be the largest of its kind, in terms of both number of users, items, and total number raw ratings. The data were collected by Yahoo! Music over a decade. We formulate a detailed recommendation model, specifically designed to account for the data set properties, its temporal dynamics, and the provided taxonomy of items. The paper demonstrates a design process that we believe to be useful at many other recommendation setups. The process is based on gradual modeling of additive components of the model, each trying to reflect a unique characteristic of the data.

Published in:

Proceedings of the IEEE  (Volume:100 ,  Issue: 9 )