Name
Abstract | ||
|---|---|---|
Recommender systems relying on latent factor models often appear as black boxes to their users. Semantic descriptions for the factors might help to mitigate this problem. Achieving this automatically is, however, a non-straightforward task due to the modelsu0027 statistical nature. We present an output-agreement game that represents factors by means of sample items and motivates players to create such descriptions. A user study shows that the collected output actually reflects real-world characteristics of the factors. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Information Retrieval | Recommender system,Information retrieval,Computer science,Factor analysis,Black box |
DocType | Volume | Citations |
Journal | abs/1808.10260 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johannes Kunkel | 1 | 0 | 2.03 |
| Benedikt Loepp | 2 | 88 | 10.71 |
| Jürgen Ziegler | 3 | 1028 | 300.31 |