| Issue |
Wuhan Univ. J. Nat. Sci.
Volume 28, Number 1, February 2023
|
|
|---|---|---|
| Page(s) | 29 - 34 | |
| DOI | https://doi.org/10.1051/wujns/2023281029 | |
| Published online | 17 March 2023 | |
Computer Science
CLC number: TP 399
News Recommendation System Based on Topic Embedding and Knowledge Embedding
1
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education/School of Cyber Science and Engineering, Wuhan University, Wuhan 430079, Hubei, China
2
Zhongnan Hospital, Wuhan University, Wuhan 430072, Hubei, China
3
Engineering Research Center of Cyberspace, Yunnan University, Kunming 650504, Yunnan, China
† To whom correspondence should be addressed. E-mail: zn000851@whu.edu.cn
Received:
18
June
2022
News recommendation system is designed to deal with massive news and provide personalized recommendations for users. Accurately capturing user preferences and modeling news and users is the key to news recommendation. In this paper, we propose a new framework, news recommendation system based on topic embedding and knowledge embedding (NRTK). NRTK handle news titles that users have clicked on from two perspectives to obtain news and user representation embedding :1) extracting explicit and latent topic features from news and mining users' preferences for them in historical behaviors; 2) extracting entities and propagating users' potential preferences in the knowledge graph. Experiments in a real-world dataset validate the effectiveness and efficiency of our approach.
Key words: news recommendation / knowledge embedding / topic embedding / historical behavior
Biography: ZHANG Haojie, male, Master candidate, research direction: artificial intelligence and big data. E-mail: haojie @ whu.edu.cn
Fundation item: Supported by the Key Research & Development Projects in Hubei Province (2022BAA041 and 2021BCA124) and the Open Foundation of Engineering Research Center of Cyberspace( KJAQ202112002)
© Wuhan University 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.