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2024 | OriginalPaper | Chapter

Bayesian Personalized Sorting Based on Time Factors and Hot Recommendations

Authors : Wenhua Zeng, Junjie Liu, Bo Zhang

Published in: Intelligent Information Processing XII

Publisher: Springer Nature Switzerland

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Abstract

The chapter introduces a Bayesian Personalized Ranking model, BPR-TH, designed to address information overload in digital libraries. By incorporating time factors and hot recommendations, BPR-TH effectively handles massive distributed data and cold start problems, outperforming traditional BPR and File-path algorithms. The model is realized through user behavior feature extraction, model construction, and optimization, resulting in improved personalized recommendations both online and offline. Experimental results demonstrate BPR-TH's superior performance in accuracy, coverage, and recall, making it a promising solution for personalized digital library recommendations.
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previous chapter Deep Friendly Embedding Space for Clustering
next chapter Design and Implementation of Risk Control Model Based on Deep Ensemble Learning Algorithm
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Metadata
Title
Bayesian Personalized Sorting Based on Time Factors and Hot Recommendations
Authors
Wenhua Zeng
Junjie Liu
Bo Zhang
Copyright Year
2024
Book
Intelligent Information Processing XII

Print ISBN: 978-3-031-57807-6

Electronic ISBN: 978-3-031-57808-3

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-57808-3_8

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