User model-based personalized recommendation algorithm for news media education resources
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...
Saved in:
Main Author: | |
---|---|
Format: | Article |
Published: |
Hindawi Ltd
2022
|
Subjects: | |
Online Access: | http://eprints.um.edu.my/42320/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.um.eprints.42320 |
---|---|
record_format |
eprints |
spelling |
my.um.eprints.423202023-10-11T12:21:32Z http://eprints.um.edu.my/42320/ User model-based personalized recommendation algorithm for news media education resources Shilin, Zhu QA Mathematics TA Engineering (General). Civil engineering (General) 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. Hindawi Ltd 2022-03-24 Article PeerReviewed Shilin, Zhu (2022) User model-based personalized recommendation algorithm for news media education resources. Mathematical Problems in Engineering, 2022. ISSN 1024-123X, DOI https://doi.org/10.1155/2022/7431948 <https://doi.org/10.1155/2022/7431948>. 10.1155/2022/7431948 |
institution |
Universiti Malaya |
building |
UM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Malaya |
content_source |
UM Research Repository |
url_provider |
http://eprints.um.edu.my/ |
topic |
QA Mathematics TA Engineering (General). Civil engineering (General) |
spellingShingle |
QA Mathematics TA Engineering (General). Civil engineering (General) Shilin, Zhu User model-based personalized recommendation algorithm for news media education resources |
description |
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. |
format |
Article |
author |
Shilin, Zhu |
author_facet |
Shilin, Zhu |
author_sort |
Shilin, Zhu |
title |
User model-based personalized recommendation algorithm for news media education resources |
title_short |
User model-based personalized recommendation algorithm for news media education resources |
title_full |
User model-based personalized recommendation algorithm for news media education resources |
title_fullStr |
User model-based personalized recommendation algorithm for news media education resources |
title_full_unstemmed |
User model-based personalized recommendation algorithm for news media education resources |
title_sort |
user model-based personalized recommendation algorithm for news media education resources |
publisher |
Hindawi Ltd |
publishDate |
2022 |
url |
http://eprints.um.edu.my/42320/ |
_version_ |
1781704627079610368 |
score |
13.19449 |