research-article

Multisource Heterogeneous User-Generated Contents-Driven Interactive Estimation of Distribution Algorithms for Personalized Search

Authors:

Yong ZhangAuthors Info & Claims

IEEE Transactions on Evolutionary Computation, Volume 26, Issue 5

Pages 844 - 858

https://doi.org/10.1109/TEVC.2021.3109576

Published: 01 October 2022 Publication History

Abstract

Personalized search is essentially a complex qualitative optimization problem, and interactive evolutionary algorithms (EAs) have been extended from EAs to adapt to solving it. However, the multisource user-generated contents (UGCs) in the personalized services have not been concerned on in the adaptation. Accordingly, we here present an enhanced restricted Boltzmann machine (RBM)-driven interactive estimation of distribution algorithms (IEDAs) with multisource heterogeneous data from the viewpoint of effectively extracting users’ preferences and requirements from UGCs to strengthen the performance of IEDA for personalized search. The multisource heterogeneous UGCs, including users’ ratings and reviews, items’ category tags, social networks, and other available information, are sufficiently collected and represented to construct an RBM-based model to extract users’ comprehensive preferences. With this RBM, the probability model for conducting the reproduction operator of estimation of distribution algorithms (EDAs) and the surrogate for quantitatively evaluating an individual (item) fitness are further developed to enhance the EDA-based personalized search. The UGCs-driven IEDA is applied to various publicly released Amazon datasets, e.g., recommendation of Digital Music, Apps for Android, Movies, and TV, to experimentally demonstrate its performance in efficiently improving the IEDA in personalized search with less interactions and higher satisfaction.

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Cited By

Larrañaga PBielza C(2024)Estimation of Distribution Algorithms in Machine Learning: A SurveyIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.331410528:5(1301-1321)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TEVC.2023.3314105
Martin ROak RSoni MMahalakshmi VSoomar AJoshi A(2023)Fusion-based Representation Learning Model for Multimode User-generated Social Network ContentJournal of Data and Information Quality10.1145/360371215:3(1-21)Online publication date: 28-Sep-2023
https://dl.acm.org/doi/10.1145/3603712
Song AWu GSuganthan PPedrycz W(2023)Automatic Variable ReductionIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319941327:4(1027-1041)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TEVC.2022.3199413

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Multisource Heterogeneous User-Generated Contents-Driven Interactive Estimation of Distribution Algorithms for Personalized Search
1. Theory of computation

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cover image IEEE Transactions on Evolutionary Computation

IEEE Transactions on Evolutionary Computation Volume 26, Issue 5

Oct. 2022

398 pages

ISSN:1089-778X

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1089-778X © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 October 2022

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Cited By

Larrañaga PBielza C(2024)Estimation of Distribution Algorithms in Machine Learning: A SurveyIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.331410528:5(1301-1321)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TEVC.2023.3314105
Martin ROak RSoni MMahalakshmi VSoomar AJoshi A(2023)Fusion-based Representation Learning Model for Multimode User-generated Social Network ContentJournal of Data and Information Quality10.1145/360371215:3(1-21)Online publication date: 28-Sep-2023
https://dl.acm.org/doi/10.1145/3603712
Song AWu GSuganthan PPedrycz W(2023)Automatic Variable ReductionIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319941327:4(1027-1041)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TEVC.2022.3199413

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