Name
Abstract | ||
|---|---|---|
Recommender systems help people cope with the problem of information
overload. A recently proposed adaptive news recommender model [Medo et al.,
2009] is based on epidemic-like spreading of news in a social network. By means
of agent-based simulations we study a "good get richer" feature of the model
and determine which attributes are necessary for a user to play a leading role
in the network. We further investigate the filtering efficiency of the model as
well as its robustness against malicious and spamming behaviour. We show that
incorporating user reputation in the recommendation process can substantially
improve the outcome. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1140/epjb/e2010-10716-5 | European Physical Journal B |
Keywords | DocType | Volume |
Recommender System,Reputation System,Malicious User,Malicious Behaviour,Recommendation Process | Journal | 80 |
Issue | ISSN | Citations |
2 | 14346036 | 13 |
PageRank | References | Authors |
0.87 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giulio Cimini | 1 | 126 | 13.77 |
| Matus Medo | 2 | 263 | 21.28 |
| Tao Zhou | 3 | 2744 | 152.77 |
| Dong Wei | 4 | 13 | 0.87 |
| Yi-Cheng Zhang | 5 | 451 | 25.98 |