Real-Time Recommendation System for Online Broadcasting Advertisement

Jeong, Chaeeun; Kang, Seongju; Chung, Kwangue

doi:10.1109/icoin50884.2021.9333921

Cited by 3 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, they proposed a novel user model that incorporates various aspects of a user’s message such as text, title, network links, and topic tags, among others, to generate a comprehensive representation of the user ( Simsek & Karagoz, 2020 ). However, Jeong, Kang & Chung (2021) proposed that only considering the user’s preferences on one platform for advertising recommendations is not enough, and it does not meet the expectations of various users. Therefore, the authors proposed an online advertising recommendation system that leverages the user’s consumption history.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, the authors proposed an online advertising recommendation system that leverages the user’s consumption history. The system calculates the similarity between users and predicts their preferences for items by comparing the rating history of similar users ( Jeong, Kang & Chung, 2021 ). Zhou et al (2019) to prevent misleading advertising from stimulating consumption, they set up a three-party game model to analyze the relationship between the government, advertising platforms, and users to prevent users from making any regretful actions due to the launch of misleading advertising.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A federated learning framework based on transfer learning and knowledge distillation for targeted advertising

Su,

Wei,

Lei

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

The rise of targeted advertising has led to frequent privacy data leaks, as advertisers are reluctant to share information to safeguard their interests. This has resulted in isolated data islands and model heterogeneity challenges. To address these issues, we have proposed a C-means clustering algorithm based on maximum average difference to improve the evaluation of the difference in distribution between local and global parameters. Additionally, we have introduced an innovative dynamic selection algorithm that leverages knowledge distillation and weight correction to reduce the impact of model heterogeneity. Our framework was tested on various datasets and its performance was evaluated using accuracy, loss, and AUC (area under the ROC curve) metrics. Results showed that the framework outperformed other models in terms of higher accuracy, lower loss, and better AUC while requiring the same computation time. Our research aims to provide a more reliable, controllable, and secure data sharing framework to enhance the efficiency and accuracy of targeted advertising.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%