Name
Abstract | ||
|---|---|---|
Knowledge-based recommenders support users in the identification of interesting items from large and potentially complex assortments. In cases where no recommendation could be found for a given set of requirements, such systems propose explanations that indicate minimal sets of faulty requirements. Unfortunately, such explanations are not personalized and do not include repair proposals which triggers a low degree of satisfaction and frequent cancellations of recommendation sessions. In this paper we present a personalized repair approach that integrates the calculation of explanations with collaborative problem solving techniques. In order to demonstrate the applicability of our approach, we present the results of an empirical study that show significant improvements in the accuracy of predictions for interesting repairs. |
Year | Venue | Keywords |
|---|---|---|
2009 | IJCAI | personalized repair approach,inconsistent requirement,interesting repair,interesting item,recommendation session,complex assortment,repair proposal,plausible repair,collaborative problem,empirical study,knowledge-based recommenders support user,faulty requirement |
Field | DocType | Citations |
Computer science,Collaborative Problem Solving,Artificial intelligence,Machine learning,Empirical research | Conference | 15 |
PageRank | References | Authors |
0.82 | 13 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander Felfernig | 1 | 1121 | 110.93 |
| Gerhard E. Friedrich | 2 | 1070 | 72.95 |
| Monika Schubert | 3 | 88 | 8.64 |
| Monika Mandl | 4 | 82 | 8.92 |
| Markus Mairitsch | 5 | 17 | 1.18 |
| Erich Teppan | 6 | 84 | 6.73 |