Estimating utility functions in the presence of response error
Management Science
Fab: content-based, collaborative recommendation
Communications of the ACM
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Personalized location-based brokering using an agent-based intermediary architecture
Decision Support Systems - Special issue: Agents and e-commerce business models
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Integrating AHP and data mining for product recommendation based on customer lifetime value
Information and Management
IEEE Transactions on Knowledge and Data Engineering
Common structure and properties of filtering systems
Electronic Commerce Research and Applications
Similarity Measure and Instance Selection for Collaborative Filtering
International Journal of Electronic Commerce
Persuasion in Recommender Systems
International Journal of Electronic Commerce
Assessing the impact of internet agent on end users' performance
Decision Support Systems
Utilizing marginal net utility for recommendation in e-commerce
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A topic-based recommender system for electronic marketplace platforms
Expert Systems with Applications: An International Journal
Opportunity model for e-commerce recommendation: right product; right time
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Prediction of members' return visit rates using a time factor
Electronic Commerce Research and Applications
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Recommender systems are useful in reducing information overload and improving decision making. Utility-based recommender systems provide recommendations based on the computation of the utility of each item for the user. Some utility-elicitation methods have been developed on the basis of multi-attribute utility theory (MAUT) to represent a decision maker's complete preference. This study investigates whether these utility-based techniques outperform the traditional content-based technique for online recommendations. A laboratory experiment was conducted in two e-commerce contexts to compare the decomposed and holistic utility-based methods, simple multi-attribute rating technique exploiting ranks (SMARTER) and radial basis function network (RBFN), with the content-based method vector space model (VSM) in terms of recommendation accuracy, time expense, and user perceptions. The results demonstrate that the performances of utility-based methods depend on recommendation contexts. Furthermore, this study proposes guidelines for choosing appropriate recommendation methods in different contexts.