Name
Abstract | ||
|---|---|---|
The 2016 ACM Recommender Systems Challenge focused on the problem of job recommendations. Given a large dataset from XING that consisted of anonymized user profiles, job postings, and interactions between them, the participating teams had to predict postings that a user will interact with. The challenge ran for four months with 366 registered teams. 119 of those teams actively participated and submitted together 4,232 solutions yielding in an impressive neck-and-neck race that was decided within the last days of the challenge. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2959100.2959207 | RecSys |
Keywords | Field | DocType |
Recommender Systems, Data Mining Challenge, XING | Recommender system,Data science,Data mining,World Wide Web,Computer science,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
6 | 0.68 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabian Abel | 1 | 1187 | 62.22 |
| András A. Benczúr | 2 | 574 | 60.14 |
| Daniel Kohlsdorf | 3 | 55 | 7.07 |
| Martha Larson | 4 | 1661 | 116.07 |
| Róbert Pálovics | 5 | 6 | 1.01 |