Fab: content-based, collaborative recommendation
Communications of the ACM
Net gain: expanding markets through virtual communities
Net gain: expanding markets through virtual communities
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
The Wasabi Personal Shopper: a case-based recommender system
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Extending the TAM for a World-Wide-Web context
Information and Management
Developing Industrial Case-Based Reasoning Applications: The Inreca Methodology
Developing Industrial Case-Based Reasoning Applications: The Inreca Methodology
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Information Systems Research
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Recommender systems using linear classifiers
The Journal of Machine Learning Research
Analysis of Trust in the E-Commerce Adoption
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Testing the determinants of microcomputer usage via a structural equation model
Journal of Management Information Systems - Special section: Navigation in information-intensive environments
Expert Systems with Applications: An International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Using contextual information and multidimensional approach for recommendation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
User credit-based collaborative filtering
Expert Systems with Applications: An International Journal
A study of the adoption of self-service technologies by consumers in convenience stores
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 3
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Are you ready for knowledge sharing? An empirical study of virtual communities
Computers & Education
The Value of TAM Antecedents in Global IS Development and Research
Journal of Organizational and End User Computing
Hi-index | 12.06 |
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.