{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T21:15:56Z","timestamp":1764018956519,"version":"3.41.0"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Interact. Intell. Syst."],"published-print":{"date-parts":[[2022,9,30]]},"abstract":"<jats:p>The traditional matrix factorisation (MF)-based recommender system methods, despite their success in making the recommendation, lack explainable recommendations as the produced latent features are meaningless and cannot explain the recommendation. This article introduces an MF-based explainable recommender system framework that utilises the user-item rating data and the available item information to model meaningful user and item latent features. These features are exploited to enhance the rating prediction accuracy and the recommendation explainability. Our proposed feature-based explainable recommender system framework utilises these meaningful user and item latent features to explain the recommendation without relying on private or outer data. The recommendations are explained to the user using text message and bar chart. Our proposed model has been evaluated in terms of the rating prediction accuracy and the reasonableness of the explanation using six real-world benchmark datasets for movies, books, video games, and fashion recommendation systems. The results show that the proposed model can produce accurate explainable recommendations.<\/jats:p>","DOI":"10.1145\/3530299","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T12:21:20Z","timestamp":1651494080000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Expressive Latent Feature Modelling for Explainable Matrix Factorisation-based Recommender Systems"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3234-1937","authenticated-orcid":false,"given":"Abdullah","family":"Alhejaili","sequence":"first","affiliation":[{"name":"King Abdulaziz University, Department of Computer Science, Loughborough University, Loughborough, Leicestershire, UK and Faculty of Computing and Information Technology, Jeddah, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6068-2942","authenticated-orcid":false,"given":"Shaheen","family":"Fatima","sequence":"additional","affiliation":[{"name":"Loughborough University, Department of Computer Science, Loughborough, Leicestershire, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,7,26]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109913"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/IRI49571.2020.00057"},{"key":"e_1_3_2_4_2","first-page":"13","volume-title":"Proceedings of the Beyond Personalization Workshop","author":"Bilgic Mustafa","year":"2005","unstructured":"Mustafa Bilgic and Raymond J. Mooney. 2005. Explaining recommendations: Satisfaction vs. promotion. In Proceedings of the Beyond Personalization Workshop. 13\u201318. Retrieved from https:\/\/grouplens.org\/beyond2005\/bp2005.pdf."},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1021240730564"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.330153"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1561\/1100000009"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/2827872"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883037"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/358916.358995"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.5121\/ijdkp.2015.5201"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-018-0558-1"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1201\/9781315108230"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/11766155_2"},{"key":"e_1_3_2_18_2","first-page":"556","volume-title":"Advances in Neural Information Processing Systems","author":"Lee Daniel","year":"2001","unstructured":"Daniel Lee and H. Sebastian Seung. 2001. Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing Systems, T. Leen, T. Dietterich, and V. Tresp (Eds.), Vol. 13. The MIT Press, Cambridge, MA, 556\u2013562. Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2000\/file\/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3_3"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2507163"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_22_2","first-page":"1257","volume-title":"Advances in Neural Information Processing Systems","author":"Mnih Andriy","year":"2008","unstructured":"Andriy Mnih and Russ R. Salakhutdinov. 2008. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems. Curran Associates Inc., 1257\u20131264."},{"key":"e_1_3_2_23_2","first-page":"5","volume-title":"Proceedings of the KDD Cup and Workshop","author":"Paterek Arkadiusz","year":"2007","unstructured":"Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. In Proceedings of the KDD Cup and Workshop. Association for Computing Machinery, New York, NY, 5\u20138."},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.03.064"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018686"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3_1"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7637-6_1"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-30164-8_887"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/336992.337035"},{"key":"e_1_3_2_30_2","first-page":"291","volume-title":"Collaborative Filtering Recommender Systems","author":"Schafer J. Ben","year":"2007","unstructured":"J. Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen. 2007. Collaborative Filtering Recommender Systems. Springer-Verlag, Berlin, 291\u2013324."},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/506443.506619"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1155\/2009\/421425"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/1502650.1502661"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1561\/1500000066"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/2600428.2609579"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-015-0897-5"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623351"},{"key":"e_1_3_2_38_2","volume-title":"Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists (1st ed.)","author":"Zheng Alice","year":"2018","unstructured":"Alice Zheng and Amanda Casari. 2018. Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists (1st ed.). O\u2019Reilly Media, Inc."},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/1060745.1060754"}],"container-title":["ACM Transactions on Interactive Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3530299","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3530299","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:24Z","timestamp":1750183764000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3530299"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,26]]},"references-count":38,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,9,30]]}},"alternative-id":["10.1145\/3530299"],"URL":"https:\/\/doi.org\/10.1145\/3530299","relation":{},"ISSN":["2160-6455","2160-6463"],"issn-type":[{"type":"print","value":"2160-6455"},{"type":"electronic","value":"2160-6463"}],"subject":[],"published":{"date-parts":[[2022,7,26]]},"assertion":[{"value":"2021-02-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-03-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-07-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}