Lessons Learned Addressing Dataset Bias in Model-Based Candidate Generation at Twitter

Understand

Traditionally, heuristic methods are used to generate candidates for large scale recommender systems.

• Model-based candidate generation promises multiple potential advantages, primarily that we can explicitly optimize the same objective as the downstream ranking model.
• However, large scale model-based candidate generation approaches suffer from dataset bias problems caused by the infeasibility of obtaining representative data on very irrelevant candidates.
• Popular techniques to correct dataset bias, such as inverse propensity scoring, do not work well in the context of candidate generation.