Summary of An Efficient Continuous Control Perspective For Reinforcement-learning-based Sequential Recommendation, by Jun Wang et al.
An Efficient Continuous Control Perspective for Reinforcement-Learning-based Sequential Recommendation
by Jun Wang, Likang Wu, Qi Liu, Yu Yang
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the problem of sequential recommendation in recommender systems (RSs). The goal is to optimize long-term user engagement by dynamically inferring user preferences from historical behaviors. To achieve this, offline reinforcement-learning-based RSs are employed, which provide an advantage over global explorations that may harm online users’ experiences. However, previous studies have mainly focused on discrete action and policy spaces, which may struggle with efficiently handling dramatically growing item sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making recommendation systems better by understanding what people like based on their past behavior. The goal is to keep people engaged in the long run. To do this, the researchers use a special type of learning that happens offline and helps avoid annoying or confusing online users. One challenge they face is dealing with very large sets of things to recommend. |
Keywords
* Artificial intelligence * Reinforcement learning
