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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jera.v8i6.9020</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Design of a Student Recommendation Platform Based on Learning Behavior and Habit Training</title><url>https://artdesignp.com/journal/JERA/8/6/10.26689/jera.v8i6.9020</url><author>ZhuXiaoyun</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>8</volume><issue>6</issue><history><date date-type="pub"><published-time>2024-12-02</published-time></date></history><abstract>This study innovatively built an intelligent analysis platform for learning behavior, which deeply integrated the cutting-edge technology of big data and Artificial Intelligence (AI), \mined and analyzed students’ learning data, and realized the personalized customization of learning resources and the accurate matching of intelligent learning partners. With the help of advanced algorithms and multi-dimensional data fusion strategies, the platform not only promotes positive interaction and collaboration in the learning environment but also provides teachers with comprehensive and in-depth students’ learning portraits, which provides solid support for the implementation of precision education and the personalized adjustment of teaching strategies. In this study, a recommender system based on user similarity evaluation and a&amp;nbsp;collaborative filtering mechanism is carefully designed, and its technical architecture and implementation process are described in detail.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Ye J, 2015, Improvement of Middle School Chemistry Teaching and Research Based on Data Analysis of Academic Quality Monitoring Platform. 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