Most recommendation methods (e.g., collaborative filtering) consist of (1) a computationally intense offline phase that computes a recommender model based on users’ opinions o...
Justin J. Levandoski, Mohamed Sarwat, Mohamed F. M...
"This book was written for my introductory course in digital audio signal processing, which I have given at the Center for Computer Research in Music and Acoustics (CCRMA) sin...
Recommender systems have been widely advocated as a way of coping with the problem of information overload for knowledge workers. Given this, multiple recommendation methods have ...
We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appr...
Mark O'Connor, Dan Cosley, Joseph A. Konstan, John...
Current conversational recommender systems are unable to offer guarantees on the quality of their recommendations due to a lack of principled user utility models. We develop an ap...