Neural Click Models for Recommender Systems
This work addresses the problem of accurately modeling user behavior for recommender system evaluation and pretraining, which is important for researchers and developers in the field.
This paper develops neural architectures, including recurrent networks and Transformer-based models, to model user behavior in recommender systems. These models outperform baselines on the ContentWise and RL4RS datasets.
We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models. Proposed architectures include recurrent networks, Transformer-based models that alleviate the quadratic complexity of self-attention, adversarial and hierarchical architectures. Our models outperform baselines on the ContentWise and RL4RS datasets and can be used in RS simulators to model user response for RS evaluation and pretraining.