| Paper ID | MLSP-18.6 |
| Paper Title |
COLD START REVISITED: A DEEP HYBRID RECOMMENDER WITH COLD-WARM ITEM HARMONIZATION |
| Authors |
Oren Barkan, Roy Hirsch, Ori Katz, Avi Caciularu, Yoni Weill, Noam Koenigstein, Microsoft, Israel |
| Session | MLSP-18: Matrix Factorization and Applications |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-MFC] Matrix factorizations/completion |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Collaborative filtering-based recommender systems are known to suffer from the item cold-start problem. Most recent attempts to mitigate this problem presented parametric approaches, such as deep content based models. In this paper, we show that a straightforward application of parametric models may lead to discrepancies between the cold and warm items' distributions in the CF space. As a remedy, we propose to combine parametric with non-parametric estimation for robust cold item placement. Extensive evaluation indicates that our method is competitive with other baselines, while producing cold items placement that better resembles the distribution of warm items in the collaborative filtering space. |
