Name
Abstract | ||
|---|---|---|
Recommender systems help users find personally relevant media content in response to an overwhelming amount of this content available digitally. A prominent issue with recommender systems is recommending new content to new users; commonly referred to as the cold start problem. It has been argued that detailed user characteristics, like personality, could be used to mitigate cold start. To explore this solution, three alternative methods measuring users' personality were compared to investigate which would be most suitable for user information acquisition. Participants (N = 60) provided user ease of use and satisfaction ratings to evaluate three different interface variants believed to measure participants' personality characteristics. Results indicated that the NEO interface and the CFG interface were promising methods for measuring personality. Results are discussed in terms of potential benefits and broader implications for recommender systems. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02247-0_25 | UMAP |
Keywords | Field | DocType |
neo interface,detailed user characteristic,new content,interface variants,new user,cfg interface,recommender system,different interface,personality acquisition,recommender systems,content available digitally,relevant media content,personality characteristic | Recommender system,World Wide Web,Cold start,Adaptive system,Computer science,Information acquisition,Usability,User information,Human–computer interaction,Cold start (automotive),Personality | Conference |
Volume | ISSN | Citations |
5535 | 0302-9743 | 21 |
PageRank | References | Authors |
1.45 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Greg Dunn | 1 | 22 | 1.86 |
| Jurgen Wiersema | 2 | 21 | 1.45 |
| Jaap Ham | 3 | 284 | 24.10 |
| Lora Aroyo | 4 | 1594 | 159.04 |