Name
Abstract | ||
|---|---|---|
A key issue in group recommendation is how to combine the individual preferences of different users that form a group and elicit a profile that accurately reflects the tastes of all members in the group. Most Group Recommender Systems (GRSs) make use of some sort of method for aggregating the preference models of individual users to elicit a recommendation that is satisfactory for the whole group. In general, most GRSs offer good results, but each of them have only been tested in one application domain. This paper describes a domain-independent GRS that has been used in two different application domains. In order to create the group preference model, we select two techniques that are widely used in other GRSs and we compare them with two novel techniques. Our aim is to come up with a model that weighs the preferences of all the individuals to the same extent in such a way that no member in the group is particularly satisfied or dissatisfied with the final recommendations. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.ins.2011.11.037 | Inf. Sci. |
Keywords | Field | DocType |
individual preference,application domain,whole group,group recommendation,group preference model,group recommender system,different user,different application domain,individual user,preference model,final recommendation,preference elicitation technique,recommender systems,artificial intelligence,software engineering | Recommender system,Data mining,Preference elicitation,sort,Human–computer interaction,Application domain,Preference learning,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
189, | 0020-0255 | 25 |
PageRank | References | Authors |
0.85 | 51 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Inma Garcia | 1 | 146 | 6.56 |
| Sergio Pajares | 2 | 43 | 2.65 |
| L. Sebastia | 3 | 296 | 24.12 |
| Eva Onaindia | 4 | 534 | 55.65 |