Аннотация
Recommender systems help users to orient in the vast space of goods, services, and events. A user interacts with the recommender engine in a sequence of exchanges of recommendations and user feedback. The idea that previous interaction influence the later ones and the importance of the sequence of interactions can be modeled using Markov decision processes and solved by reinforcement learning. Several recent articles applying reinforcement learning to recommender systems have proved the viability of this direction. But it is still difficult to compare different approaches. We propose an environment with a unified interface that will permit to compare different modelization of recommender process and different algorithms on the same underlying sequential data. We also performed the extensive parameter study for deep deterministic policy gradient methods on the well-known MovieLens dataset.
Язык оригинала | английский |
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Название основной публикации | SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings |
Издатель | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
Страницы | 862-867 |
Число страниц | 6 |
ISBN (электронное издание) | 978-1-7281-4401-6 |
ISBN (печатное издание) | 978-1-7281-4402-3 |
DOI | |
Состояние | Опубликовано - окт 2019 |
Событие | SIBIRCON 2019 International Multi-Conference - Россия, Новосибирск, Новосибирск, Российская Федерация Продолжительность: 21 окт 2019 → 27 окт 2019 Номер конференции: 8 https://sibircon.ieeesiberia.org/ |
Серия публикаций
Название | SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings |
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Конференция
Конференция | SIBIRCON 2019 International Multi-Conference |
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Сокращенный заголовок | SIBIRCON 2019 |
Страна | Российская Федерация |
Город | Новосибирск |
Период | 21.10.2019 → 27.10.2019 |
Адрес в сети Интернет |
Ключевые слова
- recommender systems
- reinforcement learning
- long-term value
- deep reinforcement learning (DRL)
- DDPG