# User recommendation system based on MIND dataset

arXiv: https://arxiv.org/abs/2209.06131 · HTML: https://mlsanity.com/paper/2209.06131/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 17 of 17)

- [Empowering News Recommendation with Pre-trained Language Models](https://mlsanity.com/paper/2104.07413/) (2021)
- [Fastformer: Additive Attention Can Be All You Need](https://mlsanity.com/paper/2108.09084/) (2021)
- [DA-Transformer: Distance-aware Transformer](https://mlsanity.com/paper/2010.06925/) (2020)
- [NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application](https://mlsanity.com/paper/2102.04887/) (2021)
- [ProFairRec: Provider Fairness-aware News Recommendation](https://mlsanity.com/paper/2204.04724/) (2022)
- [News Recommendation with Candidate-aware User Modeling](https://mlsanity.com/paper/2204.04726/) (2022)
- [Why Do We Click: Visual Impression-aware News Recommendation](https://mlsanity.com/paper/2109.12651/) (2021)
- [Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder](https://mlsanity.com/paper/2102.09206/) (2021)
- [Is News Recommendation a Sequential Recommendation Task?](https://mlsanity.com/paper/2108.08984/) (2021)
- [Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations](https://mlsanity.com/paper/2110.09905/) (2021)
- [Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation](https://mlsanity.com/paper/2104.07404/) (2021)
- [FUM: Fine-grained and Fast User Modeling for News Recommendation](https://mlsanity.com/paper/2204.04727/) (2022)
- [Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System](https://mlsanity.com/paper/2103.08458/) (2021)
- [Aspect-driven User Preference and News Representation Learning for News Recommendation](https://mlsanity.com/paper/2110.05792/) (2021)
- [Modeling Multi-interest News Sequence for News Recommendation](https://mlsanity.com/paper/2207.07331/) (2022)
- [Learning to Select Historical News Articles for Interaction based Neural News Recommendation](https://mlsanity.com/paper/2110.06459/) (2021)
- [DCAN: Diversified News Recommendation with Coverage-Attentive Networks](https://mlsanity.com/paper/2206.02627/) (2022)

## Abstract (arXiv)

Nowadays, it's a very significant way for researchers and other individuals to achieve their interests because it provides short solutions to satisfy their demands. Because there are so many pieces of information on the internet, news recommendation systems allow us to filter content and deliver it to the user in proportion to his desires and interests. RSs have three techniques: content-based filtering, collaborative filtering, and hybrid filtering. We will use the MIND dataset with our system, which was collected in 2019, the big challenge in this dataset because there is a lot of ambiguity and complex text processing. In this paper, will present our proposed recommendation system. The core of our system we have used the GloVe algorithm for word embeddings and representation. Besides, the Multi-head Attention Layer calculates the attention of words, to generate a list of recommended news. Finally, we achieve good results more than some other related works in AUC 71.211, MRR 35.72, nDCG@5 38.05, and nDCG@10 44.45.