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

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.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.26 No.5 October 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 844-858

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

ESTIMATION OF DISTRIBUTION ALGORITHM (EDA)
PERSONALIZED SEARCH
INTERACTIVE
RESTRICTED BOLTZMANN MACHINE (RBM)

Info Detail Spesifik

DOI: 10.1109/TEVC.2021.3109576

Pernyataan Tanggungjawab

Lin Bao, Xiaoyan Sun, Dunwei Gong, Yong Zhang

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