Knowledge Discovery and Recommendation With Linear Mixed Model
We give a concise tutorial on knowledge discovery with linear mixed model in movie recommendation. The versatility of mixed effects model is well explained. Commonly used methods for parameter estimation, confidence interval estimate and evaluation criteria for model selection are briefly reviewed....
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8993770/ |
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| _version_ | 1849734449595416576 |
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| author | Zhiyi Chen Shengxin Zhu Qiang Niu Tianyu Zuo |
| author_facet | Zhiyi Chen Shengxin Zhu Qiang Niu Tianyu Zuo |
| author_sort | Zhiyi Chen |
| collection | DOAJ |
| description | We give a concise tutorial on knowledge discovery with linear mixed model in movie recommendation. The versatility of mixed effects model is well explained. Commonly used methods for parameter estimation, confidence interval estimate and evaluation criteria for model selection are briefly reviewed. Mixed effects models produce sound inference based on a series of rigorous analysis. In particular, we analyze millions of movie rating data with LME4 R package and find solid evidences for a general social behavior: the young tend to be more censorious than senior people when evaluating the same object. Such a social behavior phenomenon can be used in recommender systems and business data analysis. |
| format | Article |
| id | doaj-art-c96b3bc5b61c4036a211e1ea3e694fdf |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c96b3bc5b61c4036a211e1ea3e694fdf2025-08-20T03:07:47ZengIEEEIEEE Access2169-35362020-01-018383043831710.1109/ACCESS.2020.29731708993770Knowledge Discovery and Recommendation With Linear Mixed ModelZhiyi Chen0https://orcid.org/0000-0003-4527-3785Shengxin Zhu1https://orcid.org/0000-0002-6616-6244Qiang Niu2https://orcid.org/0000-0002-6880-6874Tianyu Zuo3https://orcid.org/0000-0001-6412-6524Department of Mathematical Science, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaDepartment of Mathematical Science, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaDepartment of Mathematical Science, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaDepartment of Mathematical Science, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaWe give a concise tutorial on knowledge discovery with linear mixed model in movie recommendation. The versatility of mixed effects model is well explained. Commonly used methods for parameter estimation, confidence interval estimate and evaluation criteria for model selection are briefly reviewed. Mixed effects models produce sound inference based on a series of rigorous analysis. In particular, we analyze millions of movie rating data with LME4 R package and find solid evidences for a general social behavior: the young tend to be more censorious than senior people when evaluating the same object. Such a social behavior phenomenon can be used in recommender systems and business data analysis.https://ieeexplore.ieee.org/document/8993770/Knowledge discovery in database (KDD)linear mixed-effects model (LMM)recommender system (RS)R software |
| spellingShingle | Zhiyi Chen Shengxin Zhu Qiang Niu Tianyu Zuo Knowledge Discovery and Recommendation With Linear Mixed Model IEEE Access Knowledge discovery in database (KDD) linear mixed-effects model (LMM) recommender system (RS) R software |
| title | Knowledge Discovery and Recommendation With Linear Mixed Model |
| title_full | Knowledge Discovery and Recommendation With Linear Mixed Model |
| title_fullStr | Knowledge Discovery and Recommendation With Linear Mixed Model |
| title_full_unstemmed | Knowledge Discovery and Recommendation With Linear Mixed Model |
| title_short | Knowledge Discovery and Recommendation With Linear Mixed Model |
| title_sort | knowledge discovery and recommendation with linear mixed model |
| topic | Knowledge discovery in database (KDD) linear mixed-effects model (LMM) recommender system (RS) R software |
| url | https://ieeexplore.ieee.org/document/8993770/ |
| work_keys_str_mv | AT zhiyichen knowledgediscoveryandrecommendationwithlinearmixedmodel AT shengxinzhu knowledgediscoveryandrecommendationwithlinearmixedmodel AT qiangniu knowledgediscoveryandrecommendationwithlinearmixedmodel AT tianyuzuo knowledgediscoveryandrecommendationwithlinearmixedmodel |