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However, providing personalized recommendations from news articles, which are the sources of condense textual information is not a trivial task. A recommendation system needs to understand both the textual information of a news article, and the user contexts in terms of long-term and temporary preferences via the user\u2019s historic records. Unfortunately, many existing methods do not possess the capability to meet such need. In this work, we propose a neural deep news recommendation model called CupMar, that not only is able to learn the user-profile representation in different contexts, but also is able to leverage the multi-aspects properties of a news article to provide accurate, personalized news recommendations to users. The main components of our CupMar approach include the News Encoder and the User-Profile Encoder. Specifically, the News Encoder uses multiple properties such as news category, knowledge entity, title and body content with advanced neural network layers to derive informative news representation, while the User-Profile Encoder looks through a user\u2019s browsed news, infers both of her long-term and recent preference contexts to encode a user representation, and finds the most relevant candidate news for her. We evaluate our CupMar model with extensive experiments on the popular Microsoft News Dataset (MIND), and demonstrate the strong performance of our approach.<\/jats:p>","DOI":"10.1007\/s11280-022-01059-6","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T08:09:35Z","timestamp":1652170175000},"page":"713-732","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["CupMar: A deep learning model for personalized news recommendation based on contextual user-profile and multi-aspect article representation"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0636-377X","authenticated-orcid":false,"given":"Dai Hoang","family":"Tran","sequence":"first","affiliation":[]},{"given":"Quan Z.","family":"Sheng","sequence":"additional","affiliation":[]},{"given":"Wei Emma","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Nguyen H.","family":"Tran","sequence":"additional","affiliation":[]},{"given":"Nguyen Lu Dang","family":"Khoa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"key":"1059_CR1","doi-asserted-by":"crossref","unstructured":"Wang, S., Cao, L., Wang, Y., Sheng, Q.Z., Orgun, M.A., Lian, D.: A survey on session-based recommender systems. 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