Predicting user's multi-interests with network embedding in health-related topics

By Zhipeng Jin, Ruoran Liu, Qiudan Li, D. Zeng, Yongcheng Zhan, Lei Wang. 2016

14 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

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. Kevin Chen-Chuan Chang
2. Vincent Wenchen Zheng
3. Hongyun Cai
4. Evgeny Burnaev
5. Sergey Ivanov

In Statistical and Nonlinear Physics

1. Kevin Chen-Chuan Chang
2. Vincent Wenchen Zheng
3. Hongyun Cai
4. Sergey Ivanov
5. Evgeny Burnaev

Citations in the corpus

• A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, methodology): “As shown in the table, most studies follow the idea of DeepWalk but change the settings of either randomwalk samplingmethods ([25], [28], [62], [62]) or proximity (Definitions 5 and 6) to be preserved ([34], [47], [62], [66], [73]).”