Transfer learning in social recommendations
published: Aug. 4, 2011, recorded: July 2011, views: 1913
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Online recommendation systems are becoming more and more popular with the development of web. Data sparseness is a major problem for collaborative ﬁltering (CF) techniques in recommender systems, especially for new users and items. In this paper, we try to reduce the data sparseness in the collaborative ﬁltering problem by involving Wikipedia as an auxiliary information source. In this paper, we attempt to improve the recommendation accuracy by extracting collaborative social behavior information embedded in Wikipedia coediting history, and use this knowledge to help alleviate the data-sparsity problem in CF. In addition, we introduce a parallel computing algorithm to help scale up the transfer learning process. Our experimental results on two real world recommendation datasets show that the social co-editing knowledge in Wikipedia can be effectively transferred for CF problems.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !