Name
Abstract | ||
|---|---|---|
Past evidence has shown that generic approaches to recommender systems based upon collaborative filtering tend to poorly scale. Moreover, their fitness for scenarios supposing distributed data storage and decentralized control, like the Semantic Web, becomes largely limited for various reasons. We believe that computational trust models bear several favorable properties for social filtering, opening new opportunities by either replacing or supplementing current techniques. However, in order to provide meaningful results for recommender system applications, we expect notions of trust to clearly reflect user similarity. In this work, we therefore provide empirical results obtained from one real, operational community and verify latter hypothesis for the domain of book recommendations. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-24747-0_19 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
distributed data storage,decentralized control,semantic web,recommender system,collaborative filtering | Recommender system,Similitude,Reputation system,Collaborative filtering,Computer science,Semantic Web,Computational trust,Semantics,Distributed computing,The Internet | Conference |
Volume | ISSN | Citations |
2995 | 0302-9743 | 75 |
PageRank | References | Authors |
5.24 | 24 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cai-Nicolas Ziegler | 1 | 1507 | 83.74 |
| Georg Lausen | 2 | 3687 | 526.29 |