Search Results for author: Dik Lun Lee

Found 5 papers, 4 papers with code

Motif Enhanced Recommendation over Heterogeneous Information Network

1 code implementation26 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.

Recommendation Systems

Multi-Interest Network with Dynamic Routing for Recommendation at Tmall

3 code implementations17 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.

Recommendation Systems

C-DLSI: An Extended LSI Tailored for Federated Text Retrieval

no code implementations5 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.

Information Retrieval

Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba

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.

Graph Embedding Recommendation Systems

Side Information Fusion for Recommender Systems over Heterogeneous Information Network

1 code implementation8 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.

Collaborative Filtering Recommendation Systems

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