This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessArticle
A Hybrid News Recommendation Approach Based on Title–Content Matching
by
Shuhao Jiang
Shuhao Jiang
,
Yizi Lu
Yizi Lu
,
Haoran Song
Haoran Song ,
Zihong Lu
Zihong Lu and
Yong Zhang
Yong Zhang *
School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
*
Author to whom correspondence should be addressed.
Mathematics 2024, 12(13), 2125; https://doi.org/10.3390/math12132125 (registering DOI)
Submission received: 15 May 2024
/
Revised: 5 June 2024
/
Accepted: 2 July 2024
/
Published: 6 July 2024
Abstract
Personalized news recommendation can alleviate the information overload problem, and accurate modeling of user interests is the core of personalized news recommendation. Existing news recommendation methods integrate the titles and contents of news articles that users have historically browsed to construct user interest models. However, this method ignores the phenomenon of “title–content mismatching” in news articles, which leads to the lack of precision in user interest modeling. Therefore, a hybrid news recommendation method based on title–content matching is proposed in this paper: (1) An interactive attention network is employed to model the correlation between title and content contexts, thereby enhancing the feature representation of both; (2) The degree of title–content matching is computed using a Siamese neural network, constructing a user interest model based on title–content matching; and (3) neural collaborative filtering (NCF) based on factorization machines (FM) is integrated, taking into account the perspective of the potential relationships between users for recommendation, leveraging the insensitivity of neural collaboration to news content to alleviate the impact of title–content mismatching on user feature modeling. The proposed model was evaluated on a real-world dataset, achieving an nDCG of 83.03%, MRR of 81.88%, AUC of 85.22%, and F1 Score of 35.10%. Compared to state-of-the-art news recommendation methods, our model demonstrated an average improvement of 0.65% in nDCG and 3% in MRR. These experimental results indicate that our approach effectively enhances the performance of news recommendation systems.
Share and Cite
MDPI and ACS Style
Jiang, S.; Lu, Y.; Song, H.; Lu, Z.; Zhang, Y.
A Hybrid News Recommendation Approach Based on Title–Content Matching. Mathematics 2024, 12, 2125.
https://doi.org/10.3390/math12132125
AMA Style
Jiang S, Lu Y, Song H, Lu Z, Zhang Y.
A Hybrid News Recommendation Approach Based on Title–Content Matching. Mathematics. 2024; 12(13):2125.
https://doi.org/10.3390/math12132125
Chicago/Turabian Style
Jiang, Shuhao, Yizi Lu, Haoran Song, Zihong Lu, and Yong Zhang.
2024. "A Hybrid News Recommendation Approach Based on Title–Content Matching" Mathematics 12, no. 13: 2125.
https://doi.org/10.3390/math12132125
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article metric data becomes available approximately 24 hours after publication online.