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Efficient Deep Reinforcement Learnin...

Zhu, Zheqing.

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Efficient Deep Reinforcement Learning for Recommender Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Efficient Deep Reinforcement Learning for Recommender Systems./
Author:	Zhu, Zheqing.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	110 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: A.
Contained By:	Dissertations Abstracts International85-11A.
Subject:	Recommender systems. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31049656
ISBN:	9798382636146

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

Efficient Deep Reinforcement Learning for Recommender Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 110 p.

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

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

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.

ISBN: 9798382636146Subjects--Topical Terms:

3562220
Recommender systems.

Efficient Deep Reinforcement Learning for Recommender Systems.
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