One Backpropagation in Two Tower Recommendation Models

no code implementations • 27 Mar 2024 • Erjia Chen, Bang Wang

In this paper, we challenge such an equal training assumption and propose a novel one backpropagation updating strategy, which keeps the normal gradient backpropagation for the item encoding tower, but cuts off the backpropagation for the user encoding tower.

Reducing Popularity Bias in Recommender Systems through AUC-Optimal Negative Sampling

1 code implementation • 2 Jun 2023 • Bin Liu, Erjia Chen, Bang Wang

To achieve this win-win situation, we propose to intervene in model training through negative sampling thereby modifying model predictions.

Fairness Recommendation Systems