Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction

Current data has the characteristics of complexity and low information density, which can be called the information sparse data. However, a large amount of data makes it difficult to analyse sparse data with traditional collaborative filtering recommendation algorithms, which may lead to low accurac...

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Autores principales:	Tongyan Li, Yingxiang Li, Chen Yi-Ping Phoebe
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/d8398f96965444fcb55190d7cdfb23b2
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id	oai:doaj.org-article:d8398f96965444fcb55190d7cdfb23b2
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spelling	oai:doaj.org-article:d8398f96965444fcb55190d7cdfb23b22021-11-15T01:19:36ZNovel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction1875-919X10.1155/2021/2592604https://doaj.org/article/d8398f96965444fcb55190d7cdfb23b22021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2592604https://doaj.org/toc/1875-919XCurrent data has the characteristics of complexity and low information density, which can be called the information sparse data. However, a large amount of data makes it difficult to analyse sparse data with traditional collaborative filtering recommendation algorithms, which may lead to low accuracy. Meanwhile, the complexity of data means that the recommended environment is affected by multiple dimensional factors. In order to solve these problems efficiently, our paper proposes a multidimensional collaborative filtering algorithm based on improved item rating prediction. The algorithm considers a variety of factors that affect user ratings; then, it uses the penalty to account for users’ popularity to calculate the degree of similarity between users and cross-iterative bi-clustering for the user scoring matrix to take into account changes in user’s preferences and improves on the traditional item rating prediction algorithm, which considers user ratings according to multidimensional factors. In this algorithm, the introduction of systematic error factors based on statistical learning improves the accuracy of rating prediction, and the multidimensional method can solve data sparsity problems, enabling the strongest relevant dimension influencing factors with association rules to be found. The experiment results show that the proposed algorithm has the advantages of smaller recommendation error and higher recommendation accuracy.Tongyan LiYingxiang LiChen Yi-Ping PhoebeHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Tongyan Li Yingxiang Li Chen Yi-Ping Phoebe Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction
description	Current data has the characteristics of complexity and low information density, which can be called the information sparse data. However, a large amount of data makes it difficult to analyse sparse data with traditional collaborative filtering recommendation algorithms, which may lead to low accuracy. Meanwhile, the complexity of data means that the recommended environment is affected by multiple dimensional factors. In order to solve these problems efficiently, our paper proposes a multidimensional collaborative filtering algorithm based on improved item rating prediction. The algorithm considers a variety of factors that affect user ratings; then, it uses the penalty to account for users’ popularity to calculate the degree of similarity between users and cross-iterative bi-clustering for the user scoring matrix to take into account changes in user’s preferences and improves on the traditional item rating prediction algorithm, which considers user ratings according to multidimensional factors. In this algorithm, the introduction of systematic error factors based on statistical learning improves the accuracy of rating prediction, and the multidimensional method can solve data sparsity problems, enabling the strongest relevant dimension influencing factors with association rules to be found. The experiment results show that the proposed algorithm has the advantages of smaller recommendation error and higher recommendation accuracy.
format	article
author	Tongyan Li Yingxiang Li Chen Yi-Ping Phoebe
author_facet	Tongyan Li Yingxiang Li Chen Yi-Ping Phoebe
author_sort	Tongyan Li
title	Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction
title_short	Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction
title_full	Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction
title_fullStr	Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction
title_full_unstemmed	Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction
title_sort	novel multidimensional collaborative filtering algorithm based on improved item rating prediction
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/d8398f96965444fcb55190d7cdfb23b2
work_keys_str_mv	AT tongyanli novelmultidimensionalcollaborativefilteringalgorithmbasedonimproveditemratingprediction AT yingxiangli novelmultidimensionalcollaborativefilteringalgorithmbasedonimproveditemratingprediction AT chenyipingphoebe novelmultidimensionalcollaborativefilteringalgorithmbasedonimproveditemratingprediction
_version_	1718428951953539072

Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction

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