User model-based personalized recommendation algorithm for news media education resources
Document Type
Article
Publication Date
3-24-2022
Abstract
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.
Keywords
Media education resources, Interests of users, Spatiotemporal contextual information
Divisions
arts
Funders
None
Publication Title
Mathematical Problems in Engineering
Volume
2022
Publisher
Hindawi Ltd
Publisher Location
ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND