{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:29:31Z","timestamp":1774448971627,"version":"3.50.1"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T00:00:00Z","timestamp":1581033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["HUST: Grants No. 2019kfyXKJC021, 2019kfyXJJS091"],"award-info":[{"award-number":["HUST: Grants No. 2019kfyXKJC021, 2019kfyXJJS091"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012659","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902134, 61572215, 61872278, 61972448"],"award-info":[{"award-number":["61902134, 61572215, 61872278, 61972448"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2020,5,31]]},"abstract":"<jats:p>\n            The explosive growth of e-commerce has led to the development of the recommendation system. The recommendation system aims to provide a set of items that meet users\u2019 personalized needs through analyzing users\u2019 consumption records. However, the timeliness of purchasing data and the implicity of feedback data pose severe challenges for the existing recommendation methods. To alleviate these challenges, we exploit the user\u2019s consumption records from the perspectives of user and item, by modeling the data on both item and user level, where the item-level value reflects the grade of item, and the user-level value reflects the user\u2019s purchase intention. In this article, we collect the description information and the reviews of the items from public websites, then adopt sentiment analysis techniques to model the similarities on user level and item level, respectively. In particular, we extend the traditional latent factor model and propose two novel methods\u2014\n            <jats:bold>I<\/jats:bold>\n            tem\n            <jats:bold>L<\/jats:bold>\n            evel Similarity\n            <jats:bold>M<\/jats:bold>\n            atrix\n            <jats:bold>F<\/jats:bold>\n            actorization (ILMF) and\n            <jats:bold>U<\/jats:bold>\n            ser\n            <jats:bold>L<\/jats:bold>\n            evel Similarity\n            <jats:bold>M<\/jats:bold>\n            atrix\n            <jats:bold>F<\/jats:bold>\n            actorization (ULMF)\u2014by introducing two novel similarity measure methods. In ILMF and ULMF, the consistency between latent factors and explicit aspects is naturally incorporated into learning latent factors of the users and items, such that we can predict the users\u2019 preferences on different items more accurately. Moreover, we propose\n            <jats:bold>I<\/jats:bold>\n            tem-\n            <jats:bold>U<\/jats:bold>\n            ser\n            <jats:bold>L<\/jats:bold>\n            evel Similarity\n            <jats:bold>M<\/jats:bold>\n            atrix\n            <jats:bold>F<\/jats:bold>\n            actorization (IULMF), which combines these two methods to study their contributions on the final performance. Experimental evaluations on the real datasets show that our methods outperform the baseline approaches in terms of both the precision and NDCG.\n          <\/jats:p>","DOI":"10.1145\/3375548","type":"journal-article","created":{"date-parts":[[2020,4,4]],"date-time":"2020-04-04T01:01:06Z","timestamp":1585962066000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Time-aspect-sentiment Recommendation Models Based on Novel Similarity Measure Methods"],"prefix":"10.1145","volume":"14","author":[{"given":"Guohui","family":"Li","sequence":"first","affiliation":[{"name":"School of Software, Huazhong University of Science and Technology, Wuhan, China"}]},{"given":"Qi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8639-4570","authenticated-orcid":false,"given":"Bolong","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}]},{"given":"Nguyen Quoc Viet","family":"Hung","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Gold Coast, Australia"}]},{"given":"Pan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"}]},{"given":"Guanfeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University, Sydney, Australia"}]}],"member":"320","published-online":{"date-parts":[[2020,2,7]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"1","article-title":"Learning customer profiles for content-based filtering in e-commerce","volume":"50","author":"Abbattista F.","year":"2007","journal-title":"Commun. 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