Name
Abstract | ||
|---|---|---|
Recommender systems suggest items that might be interesting to a user. To achieve this, rating prediction is the main form of information processing that these systems perform. This article tackles the problem of predicting ratings in a group recommender system by analyzing how system accuracy is influenced by the choice of prediction approach and by a solution that employs the predicted values to avoid data sparsity. The results of more than 100 experiments show that by predicting the ratings for individual users instead of predicting them for groups, and by using these predictions in a system's group detection task, accuracy increases and problems caused by data sparsity are reduced. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MIS.2016.100 | IEEE Intelligent Systems |
Keywords | Field | DocType |
Clustering algorithms,Prediction algorithms,Adaptation models,Predictive models,Collaboration,Computational modeling,Recommender systems,Cluster approximation | Recommender system,Group detection,Data mining,Information processing,Intelligent decision support system,Computer science,Cluster analysis | Journal |
Volume | Issue | ISSN |
31 | 6 | 1541-1672 |
Citations | PageRank | References |
3 | 0.37 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ludovico Boratto | 1 | 163 | 30.91 |
| Salvatore Carta | 2 | 579 | 47.28 |
| Gianni Fenu | 3 | 92 | 27.81 |
| Fabrizio Mulas | 4 | 84 | 8.61 |
| Paolo Pilloni | 5 | 17 | 4.89 |