A Hybrid News Recommendation Approach Based on Title–Content Matching

Shuhao Jiang, Yizi Lu, Haoran Song, Zihong Lu and Yong Zhang ()
Additional contact information
Shuhao Jiang: School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
Yizi Lu: School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
Haoran Song: School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
Zihong Lu: School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
Yong Zhang: School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China

Mathematics, 2024, vol. 12, issue 13, 1-20

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.

Keywords: news recommendation; user interest models; Siamese neural network; neural collaborative filtering (NCF); factorization machines (FMs) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/13/2125/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/13/2125/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:13:p:2125-:d:1430156

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().