Efficient deep reinforcement learning for recommender systems
Abstract/Contents
- Abstract
- Current recommender systems predominantly employ supervised learning algorithms, which often fail to optimize for long-term user engagement. This short-sighted approach highlights the significance of sequential recommender systems, designed to make decisions that extend beyond immediate user responses. To maximize cumulative positive feedback, these systems must balance exploration—probing users for insightful feedback to inform future recommendations—and the strategic selection of items that pave the way for more successful future interactions. However, the dynamic nature of user behavior and evolving social trends present additional challenges, demanding that sequential recommender systems operate effectively in non-stationary environments. This necessitates a more discerning exploration strategy, focusing on gathering enduring insights rather than ephemeral information. In this dissertation, I introduce three key advancements in sequential recommender systems. These contributions are centered around scalable and intelligent exploration techniques, accommodating both immediate and delayed user feedback, while adeptly adjusting to non-stationary contexts. I provide both theoretical and empirical evidence demonstrating that our proposed algorithms outperform existing benchmarks in computational efficiency and in generating higher empirical cumulative user feedback.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Zhu, Zheqing |
|---|---|
| Degree supervisor | Van Roy, Benjamin |
| Thesis advisor | Van Roy, Benjamin |
| Thesis advisor | Sadigh, Dorsa |
| Thesis advisor | Ye, Yinyu |
| Degree committee member | Sadigh, Dorsa |
| Degree committee member | Ye, Yinyu |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Management Science and Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Zheqing Zhu. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/pg516zz1327 |
Access conditions
- Copyright
- © 2023 by Zheqing Zhu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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