Neural Click Models for Recommender Systems

Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev, Anna Volodkevich, Alexey Vasilev, A. Savchenko, Sergey Nikolenko

Abstract

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.

Locations

• arXiv (Cornell University) - View - PDF
• Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval - View