Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
The emerging role of electronic marketplaces on the Internet
Communications of the ACM
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Framework for specifying, building, and operating electronic markets
International Journal of Electronic Commerce - Special issue: Formal aspects of digital commerce
Business Models for Internet-Based B2B Electronic Markets
International Journal of Electronic Commerce
Optimal recommendation sets: covering uncertainty over user preferences
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Hi-index | 0.00 |
Recommender systems are deployed in electronic commerce (e-commerce) settings to help customers find products according to their preferences. Product recommendations may help buyers to save time by helping them choose from a variety of options. Recommendations that take into account the multiple attributes affecting a potential buyer's decision can be particularly useful in the context of Business-to-Consumer (B2C) electronic markets (e-markets). Nevertheless, multiattribute recommender systems are usually more sophisticated than single-attribute ones, and their implementation may prove complex to e-market system developers. This paper presents the design, development and evaluation of marService, a product recommendation service that is based on Multi-Attribute Utility Theory (MAUT). This approach studies the application of marService for providing wine recommendations in an existing e-market and presents the results of a simulation experiment. Using an appropriate simulation environment, the evaluation of several design options for a set of algorithms for multiattribute utility recommendation has taken place, on two synthetic data sets for wine evaluations. Based on the experience from this experiment, some general suggestions that may prove useful to e-market developers wishing to implement a marService are also provided.