CSRN: Collaborative Sequential Recommendation Networks for News Retrieval
Bing Bai, Guanhua Zhang, Ye Lin, Hao Li, Kun Bai, Bo Luo

TL;DR
This paper introduces CSRN, a neural network model that combines sequential news recommendation with collaborative filtering to better capture societal influences and improve personalization in news retrieval.
Contribution
The paper proposes a novel deep neural network framework that integrates RNN-based sequential recommendations with a fine-grained user similarity network for enhanced news recommendation.
Findings
CSRN outperforms state-of-the-art methods on public datasets.
The model effectively captures societal influences in news consumption.
Neural attention mechanisms improve neighbor influence modeling.
Abstract
Nowadays, news apps have taken over the popularity of paper-based media, providing a great opportunity for personalization. Recurrent Neural Network (RNN)-based sequential recommendation is a popular approach that utilizes users' recent browsing history to predict future items. This approach is limited that it does not consider the societal influences of news consumption, i.e., users may follow popular topics that are constantly changing, while certain hot topics might be spreading only among specific groups of people. Such societal impact is difficult to predict given only users' own reading histories. On the other hand, the traditional User-based Collaborative Filtering (UserCF) makes recommendations based on the interests of the "neighbors", which provides the possibility to supplement the weaknesses of RNN-based methods. However, conventional UserCF only uses a single similarity…
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Taxonomy
TopicsRecommender Systems and Techniques · Topic Modeling · Advanced Graph Neural Networks
