weHelp: A Reference Architecture for Social Recommender Systems

Swapneel Sheth, Nipun Arora, Christian Murphy, Gail E. Kaiser

Abstract: 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.