Aspect-Driven User Preference and News Representation Learning for News Recommendation-Reference-Cited by-同舟云学术

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Aspect-Driven User Preference and News Representation Learning for News Recommendation

Published:2022-12 Issue:12 Volume:23 Page:25297-25307
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Lu Wenpeng¹^ORCID,Wang Rongyao¹,Wang Shoujin²,Peng Xueping³^ORCID,Wu Hao⁴,Zhang Qian¹

Affiliation:

1. Department of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

2. School of Computing Technologies, RMIT University, Melbourne, VIC, Australia

3. Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, Australia

4. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

Funder

National Natural Science Foundation of China

Key Program of Science and Technology of Shandong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/9972869/09800139.pdf?arnumber=9800139

Reference43 articles.

1. Convolutional Neural Networks for Sentence Classification

2. Factorization Machines

3. MIND: A Large-scale Dataset for News Recommendation

4. Embedding-based News Recommendation for Millions of Users

5. Neural Demographic Prediction using Search Query

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2. Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework;Proceedings of the ACM Web Conference 2024;2024-05-13

3. Target-driven user preference transferring recommendation;Expert Systems with Applications;2024-03

4. News Recommendation With Word-Related Joint Topic Prediction;IEEE Access;2024

5. Intention-Aware User Modeling for Personalized News Recommendation;Database Systems for Advanced Applications;2023

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