{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T21:29:07Z","timestamp":1771190947768,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,4,28]],"date-time":"2024-04-28T00:00:00Z","timestamp":1714262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Provincial Science and Technology Department Application Fund Key Project","award":["2019YJ0455"],"award-info":[{"award-number":["2019YJ0455"]}]},{"name":"Sichuan Provincial Science and Technology Department Application Fund Key Project","award":["z2011089"],"award-info":[{"award-number":["z2011089"]}]},{"name":"Ministry of Education Chunhui Program Project","award":["2019YJ0455"],"award-info":[{"award-number":["2019YJ0455"]}]},{"name":"Ministry of Education Chunhui Program Project","award":["z2011089"],"award-info":[{"award-number":["z2011089"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The integration of graph embedding technology and collaborative filtering algorithms has shown promise in enhancing the performance of recommendation systems. However, existing integrated recommendation algorithms often suffer from feature bias and lack effectiveness in personalized user recommendation. For instance, users\u2019 historical interactions with a certain class of items may inaccurately lead to recommendations of all items within that class, resulting in feature bias. Moreover, accommodating changes in user interests over time poses a significant challenge. This study introduces a novel recommendation model, RCKFM, which addresses these shortcomings by leveraging the CoFM model, TransR graph embedding model, backdoor tuning of causal inference, KL divergence, and the factorization machine model. RCKFM focuses on improving graph embedding technology, adjusting feature bias in embedding models, and achieving personalized recommendations. Specifically, it employs the TransR graph embedding model to handle various relationship types effectively, mitigates feature bias using causal inference techniques, and predicts changes in user interests through KL divergence, thereby enhancing the accuracy of personalized recommendations. Experimental evaluations conducted on publicly available datasets, including \u201cMovieLens-1M\u201d and \u201cDouban dataset\u201d from Kaggle, demonstrate the superior performance of the RCKFM model. The results indicate a significant improvement of between 3.17% and 6.81% in key indicators such as precision, recall, normalized discount cumulative gain, and hit rate in the top-10 recommendation tasks. These findings underscore the efficacy and potential impact of the proposed RCKFM model in advancing recommendation systems.<\/jats:p>","DOI":"10.3390\/e26050371","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T10:33:36Z","timestamp":1714386816000},"page":"371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Personalized Collaborative Filtering Recommendation System Based on Bi-Graph Embedding and Causal Reasoning"],"prefix":"10.3390","volume":"26","author":[{"given":"Xiaoli","family":"Huang","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Information, Xihua University, Chengdu 610000, China"}]},{"given":"Junjie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Information, Xihua University, Chengdu 610000, China"}]},{"given":"Junying","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Information, Xihua University, Chengdu 610000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.S. 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