Sheth Swapneel Kalpesh author Columbia University. Computer Science Arora Nipun author Columbia University. Computer Science Murphy Christian author Columbia University. Computer Science Kaiser Gail E. author Columbia University. Computer Science Columbia University. Computer Science originator contributor text Technical reports New York Department of Computer Science, Columbia University 2009 English Recommender systems have become increasingly popular. Most of the research on recommender systems has focused on recommendation algorithms. There has been relatively little research, however, in the area of generalized system architectures for recommendation systems. In this paper, we introduce weHelp: a reference architecture for social recommender systems — systems where recommendations are derived automatically from the aggregate of logged activities conducted by the system's users. Our architecture is designed to be application and domain agnostic. We feel that a good reference architecture will make designing a recommendation system easier; in particular, weHelp aims to provide a practical design template to help developers design their own well-modularized systems. Computer science CUCS-024-09 http://hdl.handle.net/10022/AC:P:9299 NNC NNC 2010-07-15 15:28:05 -0400 2011-11-09 09:27:25 -0500 1783 eng