Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration

By Xuejian Wang, Lantao Yu, Kan Ren, Guanyu Tao, Weinan Zhang, Yong Yu, Jun Wang. 2017

160 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xing Xie
2. Fangzhao Wu
3. Chuhan Wu
4. Hongwei Wang
5. Yongfeng Huang
6. Wenjie Li
7. Fuzheng Zhang
8. Miao Zhao

In Information Systems

1. Xing Xie
2. Fangzhao Wu
3. Chuhan Wu
4. Yongfeng Huang
5. Miao Zhao
6. Wenjie Li
7. Hongwei Wang
8. Fuzheng Zhang

Citations in the corpus

• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, methodology): “Personalized news recommendation has attracted a lot of attention in recent years [11, 23, 45].”
• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018, background): “The idea was soon transplanted to recommender systems [4, 22, 33, 36, 47].”
• DKN: Deep Knowledge-Aware Network for News Recommendation (2018, methodology): “To characterize user’s diverse interests, we use an attention network [47, 54] to model the different impacts of the user’s clicked news on the candidate news.”
• Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model (2018, background): “We are also aware of the application of attention mechanism in recommender systems [2, 34, 35].”
• A review on deep learning for recommender systems: challenges and remedies (2018, unknown): “…et al. (2018) Zhang et al. (2018) Kim et al. (2017) Yin et al. (2017) Shankar et al. (2017) Pana et al. (2017) He et al. (2017) Zhang et al. (2017c) Wang et al. (2017) Wang et al. (2016) Xu et al. (2017a) Volkovs et al. (2017) Rassweiler Filho et al. (2017) Vall et al. (2017) Nguyen et al.…”
• Explainable Recommendation through Attentive Multi-View Learning (2019, methodology): “Deep learning-based recommendation models (Wang et al. 2017 Figure 1: Multi-level user interest extracted using our method.”
• NPA: Neural News Recommendation with Personalized Attention (2019, methodology): “Traditional news recommendation methods usually based on news relatedness [23], semantic similarities [3] and human editors’ demonstration [33].”
• R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering (2018, background): “Attention networks have recently shown remarkable success in many applications of knowledge mining and natural language processing, such as neural machine translation [3], recommendation systems [30], advertising [42], document classification [39], sentiment analysis [22], question answering [17], a”
• Deep Learning Based Recommender System (2017, methodology): “Attention-based CNNs are capable of capturing the most informative elements of the inputs [128].”