DOR: A Novel Dual-Observation-Based Approach for News Recommendation Systems

TL;DR

This paper introduces DOR, a dual-observation neural network approach for news recommendation that considers both news content and user beliefs to enhance personalization and diversity.

Contribution

The paper presents a novel dual-observation method incorporating user belief networks, improving recommendation accuracy and diversity over existing models.

Findings

Outperforms several popular baseline models.

Effectively captures user interests and biases.

Provides more personalized and diverse news recommendations.

Abstract

Online social media platforms offer access to a vast amount of information, but sifting through the abundance of news can be overwhelming and tiring for readers. personalised recommendation algorithms can help users find information that interests them. However, most existing models rely solely on observations of user behaviour, such as viewing history, ignoring the connections between the news and a user's prior knowledge. This can result in a lack of diverse recommendations for individuals. In this paper, we propose a novel method to address the complex problem of news recommendation. Our approach is based on the idea of dual observation, which involves using a deep neural network with observation mechanisms to identify the main focus of a news article as well as the focus of the user on the article. This is achieved by taking into account the user's belief network, which reflects their personal interests and biases. By considering both the content of the news and the user's perspective, our approach is able to provide more personalised and accurate recommendations. We evaluate the performance of our model on real-world datasets and show that our proposed method outperforms several popular baselines.