Name
Abstract | ||
|---|---|---|
A collaborative filtering system recommends to users products that similar users like. Collaborative filtering systems influence purchase decisions, and hence have become targets of manipulation by unscrupulous vendors. We provide theoretical and empirical results demonstrating that while common nearest neighbor algorithms, which are widely used in commercial systems, can be highly susceptible to manipulation, two classes of collaborative filtering algorithms which we refer to as linear and asymptotically linear are relatively robust. These results provide guidance for the design of future collaborative filtering systems. |
Year | Venue | Keywords |
|---|---|---|
2009 | Manipulation robustness of collaborative filtering systems | empirical result,unscrupulous vendor,commercial system,manipulation robustness,common nearest neighbor algorithm,systems influence purchase decision,similar user,users product,future collaborative |
Field | DocType | Volume |
Information theory,k-nearest neighbors algorithm,Recommender system,Collaborative filtering,Computer science,Robustness (computer science),Artificial intelligence,Machine learning | Journal | abs/0903.0064 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashish Goel | 1 | 3039 | 244.56 |
| Xiang Yan | 2 | 66 | 17.39 |