A Review on Reinforcement Learning based News Recommendation Systems and its challenges
| Autor: | Sindhuja Bangari, Shantharam Nayak, K T Rashmi, Ladly Patel |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science Perspective (graphical) 02 engineering and technology Space (commercial competition) Recommender system Data science Task (project management) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Task analysis Reinforcement learning 020201 artificial intelligence & image processing Day to day Reinforcement |
| Zdroj: | 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). |
| Popis: | Recommendation systems are helpful in both business perspective and user day to day life. These days online contents are generated in huge amount and due to this, users need a special recommendation application namely personalized News Recommendation, and it is highly challenging due to its dynamic nature. Therefore, getting a suitable and relevant news article for a user is difficult task. To address the above challenge Reinforcement Learning algorithms plays crucial role because these algorithms very much helpful in dealing with the dynamic environment and large space. This paper reviews the different Reinforcement algorithms namely Deep Q-learning network (DQN), Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG (TD3) to develop the news recommendation system and also mentioned the challenges faced by the reinforcement recommendation systems. In this study it was found that TD3 is best suited to develop the news recommendation system. |
| Databáze: | OpenAIRE |
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