Neural Click Models for Recommender Systems
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914960611737600 |
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| author | Shirokikh, Mikhail Shenbin, Ilya Alekseev, Anton Volodkevich, Anna Vasilev, Alexey Savchenko, Andrey V. Nikolenko, Sergey |
| author_facet | Shirokikh, Mikhail Shenbin, Ilya Alekseev, Anton Volodkevich, Anna Vasilev, Alexey Savchenko, Andrey V. Nikolenko, Sergey |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20055 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Click Models for Recommender Systems Shirokikh, Mikhail Shenbin, Ilya Alekseev, Anton Volodkevich, Anna Vasilev, Alexey Savchenko, Andrey V. Nikolenko, Sergey Information Retrieval Machine Learning 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. |
| title | Neural Click Models for Recommender Systems |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2409.20055 |