國立虎尾科技大學 |

Efficient Deep Reinforcement Learning for Recommender Systems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Efficient Deep Reinforcement Learning for Recommender Systems./
Author:	Zhu, Zheqing.
Description:	1 online resource (110 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: A.
Contained By:	Dissertations Abstracts International85-11A.
Subject:	Recommender systems. -
Online resource:	click for full text (PQDT)
ISBN:	9798382636146

Efficient Deep Reinforcement Learning for Recommender Systems.
Zhu, Zheqing.

Efficient Deep Reinforcement Learning for Recommender Systems. - 1 online resource (110 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: A.

Thesis (Ph.D.)--Stanford University, 2023.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382636146Subjects--Topical Terms:

1372552
Recommender systems.
Index Terms--Genre/Form:

554714
Electronic books.

Efficient Deep Reinforcement Learning for Recommender Systems.
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500 $a Advisor: Van Roy, Benjamin;Sadigh, Dorsa;Ye, Yinyu.
502 $a Thesis (Ph.D.)--Stanford University, 2023.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
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650 4 $a Distance learning. $3 1437660
650 4 $a Neural networks. $3 1011215
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