@inproceedings{raza-etal-2022-accuracy,
title = "Accuracy meets Diversity in a News Recommender System",
author = "Raza, Shaina and
Bashir, Syed Raza and
Naseem, Usman",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.332",
pages = "3778--3787",
abstract = "News recommender systems face certain challenges. These challenges arise due to evolving users{'} preferences over dynamically created news articles. The diversity is necessary for a news recommender system to expose users to a variety of information. We propose a deep neural network based on a two-tower architecture that learns news representation through a news item tower and users{'} representations through a query tower. We customize an augmented vector for each query and news item to introduce information interaction between the two towers. We introduce diversity in the proposed architecture by considering a category loss function that aligns items{'} representation of uneven news categories. Experimental results on two news datasets reveal that our proposed architecture is more effective compared to the state-of-the-art methods and achieves a balance between accuracy and diversity.",
}
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<abstract>News recommender systems face certain challenges. These challenges arise due to evolving users’ preferences over dynamically created news articles. The diversity is necessary for a news recommender system to expose users to a variety of information. We propose a deep neural network based on a two-tower architecture that learns news representation through a news item tower and users’ representations through a query tower. We customize an augmented vector for each query and news item to introduce information interaction between the two towers. We introduce diversity in the proposed architecture by considering a category loss function that aligns items’ representation of uneven news categories. Experimental results on two news datasets reveal that our proposed architecture is more effective compared to the state-of-the-art methods and achieves a balance between accuracy and diversity.</abstract>
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%0 Conference Proceedings
%T Accuracy meets Diversity in a News Recommender System
%A Raza, Shaina
%A Bashir, Syed Raza
%A Naseem, Usman
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F raza-etal-2022-accuracy
%X News recommender systems face certain challenges. These challenges arise due to evolving users’ preferences over dynamically created news articles. The diversity is necessary for a news recommender system to expose users to a variety of information. We propose a deep neural network based on a two-tower architecture that learns news representation through a news item tower and users’ representations through a query tower. We customize an augmented vector for each query and news item to introduce information interaction between the two towers. We introduce diversity in the proposed architecture by considering a category loss function that aligns items’ representation of uneven news categories. Experimental results on two news datasets reveal that our proposed architecture is more effective compared to the state-of-the-art methods and achieves a balance between accuracy and diversity.
%U https://aclanthology.org/2022.coling-1.332
%P 3778-3787
Markdown (Informal)
[Accuracy meets Diversity in a News Recommender System](https://aclanthology.org/2022.coling-1.332) (Raza et al., COLING 2022)
ACL
- Shaina Raza, Syed Raza Bashir, and Usman Naseem. 2022. Accuracy meets Diversity in a News Recommender System. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3778–3787, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.