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

arXiv: https://arxiv.org/abs/2302.01443 · HTML: https://mlsanity.com/paper/2302.01443/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 58 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [DKN: Deep Knowledge-Aware Network for News Recommendation](https://mlsanity.com/paper/1801.08284/) (2018)
- [Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://mlsanity.com/paper/1906.01195/) (2019)
- [Knowledge Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1904.12575/) (2019)
- [NPA: Neural News Recommendation with Personalized Attention](https://mlsanity.com/paper/1907.05559/) (2019)
- [Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation](https://mlsanity.com/paper/1901.08907/) (2019)
- [Personalized News Recommendation: Methods and Challenges](https://mlsanity.com/paper/2106.08934/) (2021)
- [SemGloVe: Semantic Co-occurrences for GloVe from BERT](https://mlsanity.com/paper/2012.15197/) (2020)
- [Hybrid Low-order and Higher-order Graph Convolutional Networks](https://mlsanity.com/paper/1908.00673/) (2019)
- [Attention-Based Recommendation On Graphs](https://mlsanity.com/paper/2201.05499/) (2022)

## Abstract (arXiv)

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.