Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Less is more: restructuring decisions to improve agent search
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
| Hi-index | 0.00 |
When humans face a broad spectrum of topics, where each topic consists of several options, they usually make a decision on each topic separately. Usually, a person will perform better by making a global decision, however, taking all consequences into account is extremely difficult. We present a novel computational method for advice-generation in an environment where people need to decide among multiple selection problems. This method is based on the prospect theory and uses machine learning techniques. We graphically present this advice to the users and compare it with advice which encourages the users to always select the option with a higher expected outcome. We show that our method outperforms the expected outcome approach in terms of user and satisfaction.