# A Look Into News Avoidance Through AWRS: An Avoidance-Aware Recommender System

arXiv: https://arxiv.org/abs/2407.09137 · HTML: https://mlsanity.com/paper/2407.09137/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 12 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [DKN: Deep Knowledge-Aware Network for News Recommendation](https://mlsanity.com/paper/1801.08284/) (2018)
- [NPA: Neural News Recommendation with Personalized Attention](https://mlsanity.com/paper/1907.05559/) (2019)
- [Neural News Recommendation with Attentive Multi-View Learning](https://mlsanity.com/paper/1907.05576/) (2019)
- [Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model](https://mlsanity.com/paper/2104.09617/) (2021)
- [PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity](https://mlsanity.com/paper/2106.01300/) (2021)
- [News Recommendation with Candidate-aware User Modeling](https://mlsanity.com/paper/2204.04726/) (2022)
- [Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations](https://mlsanity.com/paper/2307.06576/) (2023)
- [Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks](https://mlsanity.com/paper/1904.10367/) (2019)
- [Is News Recommendation a Sequential Recommendation Task?](https://mlsanity.com/paper/2108.08984/) (2021)
- [Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation](https://mlsanity.com/paper/2307.16089/) (2023)
- [Diversification in Session-based News Recommender Systems](https://mlsanity.com/paper/2102.03265/) (2021)
- [End-to-end Learnable Diversity-aware News Recommendation](https://mlsanity.com/paper/2204.00539/) (2022)
- [Modeling Multi-interest News Sequence for News Recommendation](https://mlsanity.com/paper/2207.07331/) (2022)

## Abstract (arXiv)

In recent years, journalists have expressed concerns about the increasing trend of news article avoidance, especially within specific domains. This issue has been exacerbated by the rise of recommender systems. Our research indicates that recommender systems should consider avoidance as a fundamental factor. We argue that news articles can be characterized by three principal elements: exposure, relevance, and avoidance, all of which are closely interconnected. To address these challenges, we introduce AWRS, an Avoidance-Aware Recommender System. This framework incorporates avoidance awareness when recommending news, based on the premise that news article avoidance conveys significant information about user preferences. Evaluation results on three news datasets in different languages (English, Norwegian, and Japanese) demonstrate that our method outperforms existing approaches.