Fab: content-based, collaborative recommendation
Communications of the ACM
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
RABBIT: An interface for database access
ACM '82 Proceedings of the ACM '82 conference
Elicitation of user preferences for multi-attribute negotiation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
LOC3 Architecture Proposal for Efficient Subscriber Localisation in Mobile Commerce Infrastructures
WMCS '05 Proceedings of the Second IEEE International Workshop on Mobile Commerce and Services
Increasing user decision accuracy using suggestions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
| Hi-index | 0.00 |
When people are seeking information they are interested in, they need time, need to know exactly what they are looking for and require attention capacities to check different sources. Recommender systems help to overcome the information overflow and filter out irrelevant sources by comparing different types of information and selecting the best results in consideration of customer preferences. Therefore accurate customer profiles are necessary which nowadays do not exist. In a mobile environment customers are not willing to spend time on disclosing their preferences; maybe they are not aware of them or have difficulties to respond to system requests. The paper in hand follows recommendations by critiquing to improve profiling quality but instead of collecting information, the transparent communication of profile extensions is focused. The customer can add preferences to his profile without explicitly expressing. Furthermore, the connection between proposed preferences and the systems conclusion behind is visible.