Name
Title | ||
|---|---|---|
Addressing Interpretability and Cold-Start in Matrix Factorization for Recommender Systems |
Abstract | ||
|---|---|---|
We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our approach is applicable on very large datasets because it exhibits almost linear complexity in the input examples and the number of co-clusters. We show, both on real industrial data and on publicly available datasets, t... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TKDE.2018.2829521 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | Field | DocType |
Recommender systems,Clustering algorithms,Collaboration,Cognition,Graphics processing units,Companies,Training | Recommender system,Interpretability,Computer science,Matrix decomposition,Implementation,Artificial intelligence,Linear complexity,Cluster analysis,Cold start (automotive),Machine learning,Speedup | Journal |
Volume | Issue | ISSN |
31 | 7 | 1041-4347 |
Citations | PageRank | References |
5 | 0.38 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michail Vlachos | 1 | 1899 | 113.39 |
| Celestine Dunner | 2 | 12 | 6.99 |
| Reinhard Heckel | 3 | 23 | 4.57 |
| V. G. Vassiliadis | 4 | 8 | 1.64 |
| Thomas P. Parnell | 5 | 7 | 2.09 |
| Kubilay Atasu | 6 | 416 | 26.73 |