{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T18:41:01Z","timestamp":1764873661292,"version":"3.41.2"},"reference-count":36,"publisher":"Emerald","issue":"8","license":[{"start":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T00:00:00Z","timestamp":1567987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2019,9,9]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles\u2019 semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users\u2019 online recommendation lists based on their current news browsing.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms\u2019 commercial value.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/imds-04-2019-0251","type":"journal-article","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T06:52:10Z","timestamp":1568184730000},"page":"1802-1818","source":"Crossref","is-referenced-by-count":10,"title":["Online news recommendations based on topic modeling and online interest adjustment"],"prefix":"10.1108","volume":"119","author":[{"given":"Duen-Ren","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yu-Shan","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Jun-Yi","family":"Lu","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"first-page":"207","article-title":"Mining association rules between sets of items in large databases","year":"1993","key":"key2019091913050044400_ref001"},{"volume-title":"Modern Information Retrieval","year":"1999","key":"key2019091913050044400_ref002"},{"issue":"3","key":"key2019091913050044400_ref003","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1145\/245108.245124","article-title":"Fab: content-based, collaborative recommendation","volume":"40","year":"1997","journal-title":"Communications of the ACM"},{"key":"key2019091913050044400_ref004","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","year":"2003","journal-title":"Journal of Machine Learning Research"},{"key":"key2019091913050044400_ref005","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","article-title":"Recommender systems survey","volume":"46","year":"2013","journal-title":"Knowledge-Based Systems"},{"issue":"4","key":"key2019091913050044400_ref006","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1023\/A:1021240730564","article-title":"Hybrid recommender systems: survey and experiments","volume":"12","year":"2002","journal-title":"User Modeling and user-adapted Interaction"},{"first-page":"27","article-title":"Semantics-based news recommendation","year":"2012","key":"key2019091913050044400_ref007"},{"issue":"4","key":"key2019091913050044400_ref008","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1108\/IMDS-04-2017-0164","article-title":"Identifying and recommending user-interested attributes with values","volume":"118","year":"2018","journal-title":"Industrial Management & Data Systems"},{"first-page":"271","article-title":"Google news personalization: scalable online collaborative filtering","year":"2007","key":"key2019091913050044400_ref009"},{"first-page":"795","article-title":"Gaussian LDA for topic models with word embeddings","year":"2015","key":"key2019091913050044400_ref010"},{"first-page":"395","article-title":"Dual collaborative topic modeling from implicit feedbacks","year":"2014","key":"key2019091913050044400_ref011"},{"issue":"S1","key":"key2019091913050044400_ref012","first-page":"5228","article-title":"Finding scientific topics","volume":"101","year":"2004","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"2","key":"key2019091913050044400_ref013","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1108\/IMDS-03-2016-0094","article-title":"Application of an improved Apriori algorithm in a mobile e-commerce recommendation system","volume":"117","year":"2017","journal-title":"Industrial Management & Data Systems"},{"issue":"2","key":"key2019091913050044400_ref014","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/335191.335372","article-title":"Mining frequent patterns without candidate generation","volume":"29","year":"2000","journal-title":"ACM Sigmod Record"},{"first-page":"549","article-title":"Fast matrix factorization for online recommendation with implicit feedback","year":"2016","key":"key2019091913050044400_ref015"},{"first-page":"61","article-title":"Content-based news recommendation","year":"2010","key":"key2019091913050044400_ref016"},{"key":"key2019091913050044400_ref017","doi-asserted-by":"crossref","unstructured":"Koren, Y. and Bell, R. 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