A recommender system is a kind of automated and sophisticated decision support system that is needed to provide a personalized solution in a brief form without going through a complicated search process. There have been a substantial number of studies to make recommender systems more accurate and efficient, however, most of them have a common critical limitation - these systems are used as virtual salespeople, rather than as marketing tools. A crucial reason for this phenomenon is that the models suggested by prior studies only focus on a user's behavioral outcomes without consideration of the embedded procedure. In this study, we propose a novel recommender system based on user's behavioral model. Our proposed system, labeled VCR virtual community recommender, recommends optimal virtual communities for an active user by case-based reasoning (CBR) using behavioral factors suggested in the technology acceptance model (TAM) and its extended models. In addition, it refines its recommendation results by considering the user's needs type at the point of usage. To test the usefulness of our recommendation model, we conducted two-step validation-empirical validation for the collected data set, and practical validation to investigate the actual satisfaction level of users. Experimental results showed that our model outperformed all comparative models from the perspective of user satisfaction. (c) 2006 Elsevier Ltd. All rights reserved.