Yizhou Sun

Yizhou Sun is a person.

• See: Bibliographic Network.

---

---

References

• https://scholar.google.com/citations?user=TQgOjK0AAAAJ

2017

• (Chen, Xue, et al., 2017) ⇒ Zhengxing Chen, Su Xue, John Kolen, Navid Aghdaie, Kazi A. Zaman, Yizhou Sun, and Magy Seif El-Nasr. (2017). “EOMM: An Engagement Optimized Matchmaking Framework.” In: Proceedings of the 26th International Conference on World Wide Web. ISBN:978-1-4503-4913-0 doi:10.1145/3038912.3052559

2015

• (Yang et al., 2015) ⇒ Yang Yang, Yizhou Sun, Jie Tang, Bo Ma, and Juanzi Li. (2015). “Entity Matching Across Heterogeneous Sources.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783353

• (Wang et al., 2015) ⇒ Weiqing Wang, Hongzhi Yin, Ling Chen, Yizhou Sun, Shazia Sadiq, and Xiaofang Zhou. (2015). “Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783335

2014

• (Yu et al., 2014) ⇒ Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, and Jiawei Han. (2014). “Personalized Entity Recommendation: A Heterogeneous Information Network Approach.” In: Proceedings of the 7th ACM International Conference on Web search and data mining, pp. 283-292 . ACM,

2009

• (Sun et al., 2009) ⇒ Yizhou Sun, Yintao Yu, and Jiawei Han. (2009). “Ranking-based Clustering of Heterogeneous Information Networks with Star Network Schema.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557107
• (Gao et al., 2009) ⇒ Jing Gao, Wei Fan, Yizhou Sun, and Jiawei Han. (2009). “Heterogeneous Source Consensus Learning via Decision Propagation and Negotiation.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557061

---