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Auteurs principaux: Liu, Yi, Xu, TianWei, Xiao, Mengjin
Format: Recurso educativo Open Access
Langue:en
Publié: 2023
Sujets:
Accès en ligne:https://eric.ed.gov/?id=EJ1393530
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author Liu, Yi
Xu, TianWei
Xiao, Mengjin
author_facet Liu, Yi
Xu, TianWei
Xiao, Mengjin
Liu, Yi
Xu, TianWei
Xiao, Mengjin
collection Education Resources Information Center
contents Small Data Fusion Algorithm for Personalized Library Recommendations Liu, Yi Xu, TianWei Xiao, Mengjin Research Libraries Data Collection Data Analysis Tables (Data) Artificial Intelligence User Needs (Information) Algorithms Comparative Analysis Library Services Web Sites Information Systems In order to better grasp the needs of library users and provide them with more accurate knowledge services, combining the characteristics of university libraries, this article applies library small data to personalized recommendation and proposes a small data fusion algorithm model for library personalized recommendation. This model combines the characteristics of small data and realizes multi-dimensional small data fusion by using fully connected neural network to capture the potential collaborative filtering information between users and projects, better grasp the needs of readers and users, and provide valuable assistance for subsequent personalized recommendation research. The effectiveness of the proposed method in personalized recommendation of library resources is verified by comparing several groups of experiments on public and self-built data sets.
format Recurso educativo Open Access
id eric_EJ1393530
institution ERIC Institute of Education Sciences
language en
publishDate 2023
record_format eric
spellingShingle Small Data Fusion Algorithm for Personalized Library Recommendations
Liu, Yi
Xu, TianWei
Xiao, Mengjin
Research Libraries
Data Collection
Data Analysis
Tables (Data)
Artificial Intelligence
User Needs (Information)
Algorithms
Comparative Analysis
Library Services
Web Sites
Information Systems
Small Data Fusion Algorithm for Personalized Library Recommendations Liu, Yi Xu, TianWei Xiao, Mengjin Research Libraries Data Collection Data Analysis Tables (Data) Artificial Intelligence User Needs (Information) Algorithms Comparative Analysis Library Services Web Sites Information Systems In order to better grasp the needs of library users and provide them with more accurate knowledge services, combining the characteristics of university libraries, this article applies library small data to personalized recommendation and proposes a small data fusion algorithm model for library personalized recommendation. This model combines the characteristics of small data and realizes multi-dimensional small data fusion by using fully connected neural network to capture the potential collaborative filtering information between users and projects, better grasp the needs of readers and users, and provide valuable assistance for subsequent personalized recommendation research. The effectiveness of the proposed method in personalized recommendation of library resources is verified by comparing several groups of experiments on public and self-built data sets.
title Small Data Fusion Algorithm for Personalized Library Recommendations
topic Research Libraries
Data Collection
Data Analysis
Tables (Data)
Artificial Intelligence
User Needs (Information)
Algorithms
Comparative Analysis
Library Services
Web Sites
Information Systems
url https://eric.ed.gov/?id=EJ1393530