Search Results for author: Yu Philip S.

Found 6 papers, 0 papers with code

Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learning

no code implementations17 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.

Community Detection

A Broad Learning Approach for Context-Aware Mobile Application Recommendation

no code implementations11 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.

Feature Importance

Composite Correlation Quantization for Efficient Multimodal Retrieval

no code implementations22 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.

Cross-Modal Retrieval Quantization

Integrating Heterogeneous Information via Flexible Regularization Framework for Recommendation

no code implementations12 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.

G-Bean: an ontology-graph based web tool for biomedical literature retrieval

no code implementations31 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.


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