Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas
Leveraging Behavioral Heterogeneity Across Markets for Cross-Market Training of Recommender Systems
Modern recommender systems are optimised to deliver personalised recommendations to millions of users spread across different geographic regions exhibiting various forms of heterogeneity, including behavioural-, content- and trend specific heterogeneity. System designers often face the challenge of deploying either a single global model across all markets, or developing custom models for different markets. In this work, we focus on the specific case of music recommendation across 21 different markets, and consider the trade-off between developing a global model versus market specific models. We begin by investigating behavioural differences across users of different markets, and motivate the need for considering market as an important factor when training models. We propose five different training styles, covering the entire spectrum of models: from a single global model to individual market specific models, and in the process, propose ways to identify and leverage users abroad, and data from similar markets. Based on a large-scale experimentation with data for 100M users across 21 different markets, we present insights which highlight that markets play a key role, and describe models that leverage market specific data in serving personalised recommendations.
Inferring the Causal Impact of New Track Releases on Music Recommendation Platforms through Counterfactual Predictions
Rishabh Mehrotra, Prasanta Bhattacharya, Mounia Lalmas
Rishabh Mehrotra, Chirag Shah, Benjamin Carterette