{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T15:52:33Z","timestamp":1770825153980,"version":"3.50.1"},"reference-count":10,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2019,3]]},"abstract":"<jats:p> The accuracy of learning resource recommendation is crucial to realizing precise teaching and personalized learning. We propose a novel collaborative filtering recommendation algorithm based on the student\u2019s online learning sequential behavior to improve the accuracy of learning resources recommendation. First, we extract the student\u2019s learning events from his\/her online learning process. Then each student\u2019s learning events are selected as the basic analysis unit to extract the feature sequential behavior sequence that represents the student\u2019s learning behavioral characteristics. Then the extracted feature sequential behavior sequence generates the student\u2019s feature vector. Moreover, we improve the H-[Formula: see text] clustering algorithm that clusters the students who have similar learning behavior. Finally, we recommend learning resources to the students combine similarity user clusters with the traditional collaborative filtering algorithm based on user. The experiment shows that the proposed algorithm improved the accuracy rate by 110% and recall rate by 40% compared with the traditional user-based collaborative filtering algorithm. <\/jats:p>","DOI":"10.1142\/s0219691319400010","type":"journal-article","created":{"date-parts":[[2018,5,22]],"date-time":"2018-05-22T05:00:51Z","timestamp":1526965251000},"page":"1940001","source":"Crossref","is-referenced-by-count":15,"title":["A learning resource recommendation algorithm based on online learning sequential behavior"],"prefix":"10.1142","volume":"17","author":[{"given":"Xuebin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Software and Microelectronics, Peking University, Beijing 100871, P. R. China"}]},{"given":"Zhengzhou","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Software and Microelectronics, Peking University, Beijing 100871, P. R. China"}]},{"given":"Jiaqi","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Software and Microelectronics, Peking University, Beijing 100871, P. R. China"}]},{"given":"Ruofei","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Software and Microelectronics, Peking University, Beijing 100871, P. R. China"}]},{"given":"DeQi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software and Microelectronics, Peking University, Beijing 100871, P. R. China"}]},{"given":"Qun","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Software and Microelectronics, Peking University, Beijing 100871, P. R. 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