Neural Click Models for Recommender Systems

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.

References in corpus (5)

• Data Synthesis based on Generative Adversarial Networks
• Causal Intervention for Leveraging Popularity Bias in Recommendation
• Generative Adversarial Networks for Spatio-temporal Data: A Survey
• An Adversarial Imitation Click Model for Information Retrieval
• ContentWise Impressions: An Industrial Dataset with Impressions Included

Cited by in corpus (1)

• Rethinking Click Models in Light of Carousel Interfaces: Theory-Based Categorization and Design of Click Models