Construction of Personalized Service System of Digital Resource Library Based on Artificial Intelligence

Personalized service is a targeted initiative for digital resource libraries to improve the quality of service and better play the function of culture and education.This paper proposes a digital book personalized recommendation algorithm based on artificial intelligence technology.After acquiring the borrowing data and pre-processing, the reader's portrait is visualized with factor analysis and cluster analysis methods respectively.The traditional Slopeone algorithm is weighted and the collaborative filtering algorithm is improved.Combine the user profile with collaborative filtering to realize the personalized recommendation of digital books.User similarity calculates four types of readers such as pragmatic, youthful, recreational and curious.This paper's algorithm outperforms CFRA and RABC algorithms under each parameter, with the highest recommendation accuracy and novelty, and realizes personalized library services.

Publication Details

Journal
Journal of Combinatorial Mathematics and Combinatorial Computing
Published
2025-03-30
DOI
https://doi.org/10.61091/jcmcc127a-529
Citations
1
Primary Topic
Medical Research and Treatments
Type
article
Field-Weighted Citation Impact
2.38
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Construction of Personalized Service System of Digital Resource Library Based on Artificial Intelligence

1 citations
Journal of Combinatorial Mathematics and Combinatorial Computing
Medical Research and Treatments
2.38
article

Construction of Personalized Service System of Digital Resource Library Based on Artificial Intelligence

article en
1 citations

Abstract

Personalized service is a targeted initiative for digital resource libraries to improve the quality of service and better play the function of culture and education.This paper proposes a digital book personalized recommendation algorithm based on artificial intelligence technology.After acquiring the borrowing data and pre-processing, the reader's portrait is visualized with factor analysis and cluster analysis methods respectively.The traditional Slopeone algorithm is weighted and the collaborative filtering algorithm is improved.Combine the user profile with collaborative filtering to realize the personalized recommendation of digital books.User similarity calculates four types of readers such as pragmatic, youthful, recreational and curious.This paper's algorithm outperforms CFRA and RABC algorithms under each parameter, with the highest recommendation accuracy and novelty, and realizes personalized library services.

Journal of Combinatorial Mathematics and Combinatorial ComputingVol. 127a
Openalex Percentile: Top 14%
Medical Research and Treatments
2.38
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.