Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)

<Previous Article In Volume
Next Article In Volume>

Personalized News Recommendations Based on NRMS

Authors
Xiao Li1, Zhong Xu1, Heping Peng1, *, Hongbin Wang1, Qingdan Huang1
1Guangzhou Power Supply Bureau, Guangdong Power Grid Co. Ltd., Guangzhou, China
*Corresponding author. Email: papercrane@263.net
Corresponding Author
Heping Peng
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-198-2_16How to use a DOI?
Keywords
Component; News recommendation; News modeling; Attention
Abstract

Personalized news recommendations are a crucial technology for assisting users in discovering news content of their interest and reducing information overload. At the same time, personalized news recommendations allow for effective statistics on news data and will provide assistance in the application of news data across industries and domains. In this regard, accurate modeling of news and users is crucial, while capturing words and the context of news is important for learning news and user representation. Recently, attention models have been widely used and their effectiveness in capturing contextual information is better than that of traditional CNN models. However, the combination of attention models with personalized news recommendation still needs further research. In this article, we have discussed personalized news recommendation using NRMS models and analyzed the application of attention models in personalized news recommendation. We have also proposed several directions for further research to assist researchers in gaining a comprehensive understanding of the implementation of attention models in personalized news recommendation.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial 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 license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
10 August 2023
ISBN
978-94-6463-198-2
ISSN
2589-4900
DOI
10.2991/978-94-6463-198-2_16How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial 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 license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Xiao Li
AU  - Zhong Xu
AU  - Heping Peng
AU  - Hongbin Wang
AU  - Qingdan Huang
PY  - 2023
DA  - 2023/08/10
TI  - Personalized News Recommendations Based on NRMS
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 137
EP  - 148
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_16
DO  - 10.2991/978-94-6463-198-2_16
ID  - Li2023
ER  -