Name
Abstract | ||
|---|---|---|
Deep learning-based recommendation has become a widely adopted technique in various online applications. Typically, a deployed model undergoes frequent re-training to capture users' dynamic behaviors from newly collected interaction logs. However, the current model training process only acquires users' feedbacks as labels, but fails to take into account the errors made in previous recommendations. Inspired by the intuition that humans usually reflect and learn from mistakes, in this paper, we attempt to build a self-correction continual learning loop (dubbed ReLoop) for recommender systems. In particular, a new customized loss is employed to encourage every new model version to reduce prediction errors over the previous model version during training. Our ReLoop learning framework enables a continual self-correction process in the long run and thus is expected to obtain better performance over existing training strategies. Both offline experiments and an online A/B test have been conducted to validate the effectiveness of ReLoop. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3477495.3531922 | SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Keywords | DocType | Citations |
Recommender System, CTR prediction, Self-Correction | Conference | 1 |
PageRank | References | Authors |
0.34 | 16 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guohao Cai | 1 | 1 | 0.34 |
| Jieming Zhu | 2 | 1 | 0.68 |
| Quanyu Dai | 3 | 5 | 2.76 |
| Zhenhua Dong | 4 | 7 | 3.49 |
| Xiuqiang He | 5 | 312 | 39.21 |
| Ruiming Tang | 6 | 125 | 19.25 |
| Rui Zhang | 7 | 1 | 1.36 |