Collaborative Intent Prediction with Real-Time Contextual Data

By Yu Sun, Nicholas Jing Yuan, Xing Xie, Kieran McDonald, Rui Zhang. 2017

68 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Transportation

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. Xiangnan He
2. Xiang Wang
3. Xing Xie
4. Fuzheng Zhang
5. Hongwei Wang
6. Wenjie Li
7. Miao Zhao
8. Minyi Guo

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Xing Xie
4. Miao Zhao
5. Hongwei Wang
6. Wenjie Li
7. Fuzheng Zhang
8. Minyi Guo

In Transportation

1. Xing Xie
2. Xiang Wang
3. Miao Zhao
4. Richang Hong
5. Hongwei Wang
6. Wenjie Li
7. Xiangnan He
8. Yu Zheng

Citations in the corpus

• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018, background): “To address these limitations, researchers have proposed incorporating side information into CF, such as social networks [9], user/item attributes [32], images [43] and contexts [26].”
• KDGAN: Knowledge Distillation with Generative Adversarial Networks (2018, methodology): “We can also apply KDGAN to address the other problems where privileged provision is available [44].”
• Deep Item-based Collaborative Filtering for Top-N Recommendation (2018, background): “Researchers in References [23, 24, 26, 35, 37, 41] introduced extra information like review texts and social relations into MF to address the rating sparsity issue.”