EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911885464436736 |
|---|---|
| author | Yu, Yuanqing Gao, Chongming Chen, Jiawei Tang, Heng Sun, Yuefeng Chen, Qian Ma, Weizhi Zhang, Min |
| author_facet | Yu, Yuanqing Gao, Chongming Chen, Jiawei Tang, Heng Sun, Yuefeng Chen, Qian Ma, Weizhi Zhang, Min |
| contents | Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising attention for their potential to enhance long-term user engagement. However, research in this field faces challenges, including the lack of user-friendly frameworks, inconsistent evaluation metrics, and difficulties in reproducing existing studies. To tackle these issues, we introduce EasyRL4Rec, an easy-to-use code library designed specifically for RL-based RSs. This library provides lightweight and diverse RL environments based on five public datasets and includes core modules with rich options, simplifying model development. It provides unified evaluation standards focusing on long-term outcomes and offers tailored designs for state modeling and action representation for recommendation scenarios. Furthermore, we share our findings from insightful experiments with current methods. EasyRL4Rec seeks to facilitate the model development and experimental process in the domain of RL-based RSs. The library is available for public use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15164 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems Yu, Yuanqing Gao, Chongming Chen, Jiawei Tang, Heng Sun, Yuefeng Chen, Qian Ma, Weizhi Zhang, Min Information Retrieval Machine Learning Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising attention for their potential to enhance long-term user engagement. However, research in this field faces challenges, including the lack of user-friendly frameworks, inconsistent evaluation metrics, and difficulties in reproducing existing studies. To tackle these issues, we introduce EasyRL4Rec, an easy-to-use code library designed specifically for RL-based RSs. This library provides lightweight and diverse RL environments based on five public datasets and includes core modules with rich options, simplifying model development. It provides unified evaluation standards focusing on long-term outcomes and offers tailored designs for state modeling and action representation for recommendation scenarios. Furthermore, we share our findings from insightful experiments with current methods. EasyRL4Rec seeks to facilitate the model development and experimental process in the domain of RL-based RSs. The library is available for public use. |
| title | EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2402.15164 |