no code implementations • 17 Oct 2018 • Chen Jianguo, Li Kenli, Bilal Kashif, Metwally Ahmed A., Li Keqin, Yu Philip S.
It is critical to integrate multiple data resources to identify reliable protein communities that have biological significance and improve the performance of community detection methods for large-scale PPI networks.
no code implementations • 18 May 2018 • Zheng Lei, Lu Chun-Ta, He Lifang, Xie Sihong, Noroozi Vahid, Huang He, Yu Philip S.
In this paper, we study the problem of modeling users' diverse interests.
no code implementations • 11 Sep 2017 • Liang Tingting, He Lifang, Lu Chun-Ta, Chen Liang, Yu Philip S., Wu Jian
With the rapid development of mobile apps, the availability of a large number of mobile apps in application stores brings challenge to locate appropriate apps for users.
no code implementations • 22 May 2016 • Long Mingsheng, Cao Yue, Wang Jianmin, Yu Philip S.
Efficient similarity retrieval from large-scale multimodal database is pervasive in modern search engines and social networks.
no code implementations • 12 Nov 2015 • Shi Chuan, Liu Jian, Zhuang Fuzhen, Yu Philip S., Wu Bin
The experiments also reveal that different regularization models have obviously different impact on users and items.
no code implementations • 31 Aug 2015 • Wang James Z., Zhang Yuanyuan, Dong Liang, Li Lin, Srimani Pradip K, Yu Philip S.
To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. G-Bean addresses PubMed's limitations with three innovations: parallel document index creation, ontology-graph based query expansion, and retrieval and re-ranking of documents based on user's search intention. Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database.