Data Bootstrapping for Interactive Recommender Systems

Ajay Joshi
Ajit Apte
Anand Kesari
Anushya Subbiah
Dima Kuzmin
John Anderson
Li Zhang
Marty Zinkevich
The 2nd International Workshop on Online and Adaptive Recommender Systems (2022)

Abstract

Modifying recommender systems for new kinds of user interactions is costly and exploration is slow since machine learning models can be trained and evaluated on live data only after a product supporting these new interactions is deployed. Our data bootstrapping approach moves the task of developing models for new interactions into the input representation allowing a standard machine learning model (e.g. a transformer model) to be used to train a model capturing the new interactions. More specifically, we use data obtained from a launched system to generate simulated data that includes the new interactions options. This approach helps accelerate model and algorithm development, and reduce the time to launch new interaction experiences. We present machine learning methods designed specifically to work well with limited and noisy data produced via data bootstrapping.

Research Areas