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Type: Journal article
Title: CupMar: A deep learning model for personalized news recommendation based on contextual user-profile and multi-aspect article representation
Author: Tran, D.H.
Sheng, Q.Z.
Zhang, W.E.
Tran, N.H.
Khoa, N.L.D.
Citation: World Wide Web, 2023; 26(2):713-732
Publisher: Springer
Issue Date: 2023
ISSN: 1386-145X
Statement of
Dai Hoang Tran, Quan Z. Sheng, Wei Emma Zhang, Nguyen H. Tran, Nguyen Lu Dang Khoa
Abstract: In modern days, making recommendation for news articles poses a great challenge due to vast amount of online information. 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’s 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’s 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.
Keywords: Recommendation systems
News recomendation
Personalized recommendation
Neural networks
Attention mechanism
Contextual profiles
Multi-aspect news
Description: Published online: 10 May 2023
Rights: © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons. org/ licenses/ by/4. 0/.
DOI: 10.1007/s11280-022-01059-6
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Appears in Collections:Computer Science publications

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