News Recommendation Algorithm Based on Multiple Perspectives

Fan, Linjuan; Xu, Fei; Sun, Yongyong; Zhou, Hanghang

doi:10.1109/iccnea57056.2022.00016

Cited by 3 publications

(3 citation statements)

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“…However, there is a lack of interaction between candidate news, hot information, and user information, making it challenging to balance personalized recommendations while maintaining appropriate attention to hot news, resulting in a reduction in the diversity of the recommendation system. (F. Xu et al, 2022) introduce based on Multiple Perspectives (BTEC). This method uses the BERT model to vectorize the body, title, events, and hotspots.…”

Section: Hotspot-based News Recommendationmentioning

confidence: 99%

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A Hotspot-Aware Personalized News Recommendation Mechanism Based on DistilBERT-TC-MA

He,

Wang

2024

International Journal of Distributed Systems and Technologies

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Aiming at the problems of existing news recommendation methods, such as inadequate exploration of the semantic information of news, neglecting potential hotspot features of news, and challenging the balance between user preferences and hotspot features, a hotspot-aware personalized news recommendation model (DistilBERT-TC-MA) is suggested, which integrates the distilled version of BERT (DistilBERT), text convolutional neural network (TextCNN), and multilayer attention (MA). First, it takes full advantage of DistilBERT, TextCNN, and self-attention mechanism to achieve news encoding. Following this, representations of trending news are dynamically aggregated using the attention mechanism, while user preferences are mined utilizing user click history. Finally, in order to successfully accomplish the click prediction of candidate news, the hotspot features, user preferences, and candidate news are ultimately combined using a click predictor. The experimental results of the suggested DistilBERT-TC-MA model on MIND dataset are better than several other advanced methods.

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Section: Hotspot-based News Recommendationmentioning

confidence: 99%

“…BTEC(F. Xu et al, 2022) utilizes the BERT model to vectorize news content, including the body, title, events, and hot topics. It integrates these vectors from four perspectives to enhance the effectiveness of news recommendations.…”

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confidence: 99%

A Hotspot-Aware Personalized News Recommendation Mechanism Based on DistilBERT-TC-MA

He,

Wang

2024

International Journal of Distributed Systems and Technologies

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“…Wang et al 5 proposed to learn the user representation from the news content and title of user's historical clicks and evaluate the correlation between the clicked news and the candidate news through a knowledge-aware Convolutional Neural Network (CNN). Fan et al 6 proposed the News Recommendation Algorithm Based on Multiple Perspectives (BTEC), which utilizes the Bidirectional Encoder Representations from Transformers (Bert) model and the attention mechanism to vectorize the content, titles, and events in the news, respectively, and performs fusion processing for candidate news as well as news browsed by the user's history based on the above view. As a matter of fact, some news platforms, in order to gain traffic or promote advertisement for profit, publish news with exaggerated titles, aiming to cause readers to click on the news, however, the actual news content is not exactly a reflection of the headline, leading to the issue of "title-content mismatching".…”

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confidence: 99%

A Hybrid News Recommendation Approach Based on Title-Content Matching

Jiang,

Lu,

Song

et al. 2024

Preprint

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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 dataset, and the experimental results demonstrate that the proposed method effectively improved the performance of news recommendation.

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A Hybrid News Recommendation Approach Based on Title–Content Matching

Jiang,

Lu,

Song

et al. 2024

Mathematics

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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.

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News Recommendation Algorithm Based on Multiple Perspectives

Cited by 3 publications

References 0 publications

A Hotspot-Aware Personalized News Recommendation Mechanism Based on DistilBERT-TC-MA

A Hotspot-Aware Personalized News Recommendation Mechanism Based on DistilBERT-TC-MA

A Hybrid News Recommendation Approach Based on Title-Content Matching

A Hybrid News Recommendation Approach Based on Title–Content Matching

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