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Found 6 papers, 1 papers with code
User-item matching for recommendation fairness
As we all know, users and item-providers are two main parties of participants in recommender systems.
Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks
Experimental results showed that our framework achieved the top performance on ISLES 2018 challenge and: 1) our method using synthesized pseudo DWI outperformed methods segmenting the lesion from perfusion parameter maps directly; 2) the feature extractor exploiting additional spatiotemporal CTA images led to better synthesized pseudo DWI quality and higher segmentation accuracy; and 3) the proposed loss functions and network structure improved the pseudo DWI synthesis and lesion segmentation performance.
Improving Recommendation Diversity by Highlighting the ExTrA Fabricated Experts
Nowadays, recommender systems (RSes) are becoming increasingly important to individual users and business marketing, especially in the online e-commerce scenarios.
Alleviating the recommendation bias via rank aggregation
The primary goal of a recommender system is often known as "helping users find relevant items", and a lot of recommendation algorithms are proposed accordingly.
OpenHowNet: An Open Sememe-based Lexical Knowledge Base
In this paper, we present an open sememe-based lexical knowledge base OpenHowNet.
Resource Mention Extraction for MOOC Discussion Forums
We propose the novel problem of learning resource mention identification in MOOC forums.
