Name
Abstract | ||
|---|---|---|
The vast amounts of information presented in museums can be overwhelming to a visitor, whose receptivity and time are typically limited. Hence, s/he might have difficulties selecting interesting exhibits to view within the available time. Mobile, context-aware guides offer the opportunity to improve a visitor's experience by recommending exhibits of interest, and personalising the delivered content. The first step in this recommendation process is the accurate prediction of a visitor's activities and preferences. In this paper, we present two adaptive collaborative models for predicting a visitor's next locations in a museum, and an ensemble model that combines their predictions. Our experimental results from a study using a small dataset of museum visits are encouraging, with the ensemble model yielding the best performance overall. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-70987-9_7 | AH |
Keywords | Field | DocType |
next location,context-aware guide,collaborative models,adaptively predict visitor locations,accurate prediction,adaptive collaborative model,interesting exhibit,best performance overall,museum visit,ensemble model,available time | Recommender system,World Wide Web,Ensemble forecasting,Computer science,Human–computer interaction,User modeling,Visitor pattern | Conference |
Volume | ISSN | Citations |
5149 | 0302-9743 | 9 |
PageRank | References | Authors |
0.66 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabian Bohnert | 1 | 198 | 13.97 |
| Ingrid Zukerman | 2 | 994 | 113.39 |
| Shlomo Berkovsky | 3 | 1027 | 86.12 |
| Timothy Baldwin | 4 | 452 | 22.18 |
| Liz Sonenberg | 5 | 802 | 119.89 |