Name
Abstract | ||
|---|---|---|
Despite the fact that stereotyping has been used many times in recommender systems, little is known about why stereotyping
is successful for some users but unsuccessful for others. To begin to address this issue, we conducted experiments in which
stereotype-based user models were automatically constructed and the performance of overall user models and individual stereotypes
observed. We have shown how concepts from data fusion, a previously unconnected field, can be applied to illustrate why the
performance of stereotyping varies between users. Our study illustrates clearly that the interactions between stereotypes,
in terms of their ratings of items, is a major factor in overall user model performance and that poor performance on the part
of an individual stereotype need not directly cause poor overall user model performance.
|
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11768012_19 | Adaptive Hypermedia and Adaptive Web-Based Systems |
Keywords | Field | DocType |
poor performance,data fusion,recommender system,overall user model performance,poor overall user model,overall user model,major factor,unconnected field,individual stereotype,user model,stereotype,distributed system,adaptability | Recommender system,Adaptability,Hypermedia,Computer science,Sensor fusion,Human–computer interaction,User modeling,Stereotype,Artificial intelligence,Distributed computing | Conference |
Volume | ISSN | ISBN |
4018 | 0302-9743 | 3-540-34696-1 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zoë P. Lock | 1 | 0 | 0.68 |
| Daniel Kudenko | 2 | 678 | 84.54 |