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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\u2019 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\u2019s 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.<\/jats:p>","DOI":"10.1155\/2021\/2592604","type":"journal-article","created":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T21:50:09Z","timestamp":1636149009000},"page":"1-14","source":"Crossref","is-referenced-by-count":4,"title":["Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3741-4026","authenticated-orcid":true,"given":"Tongyan","family":"Li","sequence":"first","affiliation":[{"name":"Chengdu University of Information Technology, Department of Communication Engineering, Chengdu 610225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingxiang","family":"Li","sequence":"additional","affiliation":[{"name":"Chengdu University of Information Technology, Department of Communication Engineering, Chengdu 610225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Yi-Ping Phoebe","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-018-01402-3"},{"issue":"4","key":"2","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1109\/TCYB.2018.2800731","article-title":"User participation in collaborative filtering-based recommendation systems: a game theoretic approach","volume":"49","author":"L. 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