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Found 6 papers, 0 papers with code
G-Bean: an ontology-graph based web tool for biomedical literature retrieval
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.
Integrating Heterogeneous Information via Flexible Regularization Framework for Recommendation
The experiments also reveal that different regularization models have obviously different impact on users and items.
Composite Correlation Quantization for Efficient Multimodal Retrieval
Efficient similarity retrieval from large-scale multimodal database is pervasive in modern search engines and social networks.
A Broad Learning Approach for Context-Aware Mobile Application Recommendation
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.
MARS: Memory Attention-Aware Recommender System
In this paper, we study the problem of modeling users' diverse interests.
Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learning
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.
