User model-based personalized recommendation algorithm for news media education resources

Shilin, Zhu (2022) User model-based personalized recommendation algorithm for news media education resources. Mathematical Problems in Engineering, 2022. ISSN 1024-123X, DOI

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Traditional recommendations for news and media education resources usually ignore the importance of sequential patterns in user check-in behavior and fail to effectively capture the complex and dynamically changing interests of users. As a result, this study provides a recommendation model for news and media education materials based on a user model. To capture changes in users' interests, the model can represent and fuse short-term and long-term preferences separately. For short-term preferences, a long- and short-term memory network incorporating spatiotemporal contextual information is proposed to learn complex sequential transfer patterns in users' check-in behaviors and further extract short-term preferences accurately through a goal-based attention mechanism. A user attention-based approach is utilized to capture fine-grained links between users and interest points for long-term preferences. Finally, experimental simulations are conducted on two datasets, Foursquare and Gowalla. The results show that the proposed user model-based recommendation model for news media education resources has better performance compared with the mainstream recommendation methods on different evaluation criteria, which validates the effectiveness of the proposed model.

Item Type: Article
Funders: None
Uncontrolled Keywords: Media education resources; Interests of users; Spatiotemporal contextual information
Subjects: Q Science > QA Mathematics
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Arts and Social Sciences
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 11 Oct 2023 12:21
Last Modified: 11 Oct 2023 12:21

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