Neural Click Models for Recommender Systems
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 58 references that are in the map. First posted on arXiv 30 Sep 2024.
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23 of 58 references.
References · 23
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