no code implementations • 29 Jan 2024 • Pengfei Zhao, Haoren Zhu, Wilfred Siu Hung NG, Dik Lun Lee
With the equivalence relationship established, we introduce an innovative approach, named GARCH-NN, for constructing NN-based volatility models.
no code implementations • 18 Nov 2022 • Haoren Zhu, Hao Ge, Xiaodong Gu, Pengfei Zhao, Dik Lun Lee
Traditional recommender systems are typically passive in that they try to adapt their recommendations to the user's historical interests.
1 code implementation • 26 Aug 2019 • Huan Zhao, Yingqi Zhou, Yangqiu Song, Dik Lun Lee
In this paper, we propose to use motifs to capture higher-order relations among nodes of same type in a HIN and develop the motif-enhanced meta-path (MEMP) to combine motif-based higher-order relations with edge-based first-order relations.
5 code implementations • 17 Apr 2019 • Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Pipei Huang, Huan Zhao, Guoliang Kang, Qiwei Chen, Wei Li, Dik Lun Lee
Industrial recommender systems usually consist of the matching stage and the ranking stage, in order to handle the billion-scale of users and items.
Ranked #1 on Information Retrieval on Amazon
no code implementations • 5 Oct 2018 • Qijun Zhu, Dandan Li, Dik Lun Lee
Different from existing centralized information retrieval (IR) methods, in which search is done on a logically centralized document collection, FTR is composed of a number of peers, each of which is a complete search engine by itself.
2 code implementations • KDD 2018 • Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, Dik Lun Lee
Using online A/B test, we show that the online Click-Through-Rate (CTRs) are improved comparing to the previous recommendation methods widely used in Taobao, further demonstrating the effectiveness and feasibility of our proposed methods in Taobao's live production environment.
1 code implementation • 8 Jan 2018 • Huan Zhao, Quanming Yao, Yangqiu Song, James Kwok, Dik Lun Lee
Collaborative filtering (CF) has been one of the most important and popular recommendation methods, which aims at predicting users' preferences (ratings) based on their past behaviors.