In this paper, we first provide a comprehensive investigation of four online job recommender systems (JRSs) from four different aspects: user profiling, recommendation strategies, recommendation output, and user feedback. In particular, we summarize the pros and cons of these online JRSs and highlight their differences. We then discuss the challenges in building high-quality JRSs. One main challenge lies on the design of recommendation strategies since different job applicants may have different characteristics. To address the aforementioned challenge, we develop an online JRS, iHR, which groups users into different clusters and employs different recommendation approaches for different user clusters. As a result, iHR has the capability of choosing the appropriate recommendation approaches according to users' characteristics. Empirical results demonstrate the effectiveness of the proposed system.
Pathogenic H7N9 influenza viruses continue to pose a public health concern. The H7N9 virus has caused five outbreak waves of human infections in China since 2013. In the present study, a novel H7N9 strain (A/Guangdong/8H324/2017) was isolated from a female patient with severe respiratory illness during the fifth wave of the 2017 H7N9 epidemic. Phylogenetic analysis showed that the H7N9 viruses collected during the fifth wave belong to two different lineages: the Pearl River Delta lineage and the Yangtze River Delta lineage. The novel isolate is closely related to the Pearl River Delta H7N9 viruses, which were isolated from patients in Guangdong Province. The novel H7N9 isolate has an insertion of three basic amino acids in the cleavage site of hemagglutinin (HA), which may enhance virulence in poultry. The 2017 isolate also possesses an R292K substitution in the neuraminidase (NA) protein, which confers oseltamivir resistance. This study highlights the pandemic potential of the novel H7N9 virus in mammals; thus, future characterization and surveillance is warranted.
Cross-domain recommendation is an effective technique to alleviate the data sparsity problem in recommender systems by utilizing the information from relevant domains. In this paper, we propose Crossdomain Deep Neural Network (CD-DNN) for the cross-domain recommendation. CD-DNN solves the rating prediction problem by modeling users and items using reviews and item metadata, which jointly learns features of users and items from not only the target domain but also other source domains. Latent factors for users and items are learned by several parallel neural networks, and the relevance of user features and item features is learned by maximizing prediction accuracy. CD-DNN builds a single mapping for user features in the latent space, so that the network for user is optimized together with item features from other domains. Experimental results indicate that the proposed CD-DNN significantly outperforms other state-of-the-art recommendation approaches on four public datasets of Amazon and it alleviates the data sparsity problem by leveraging more data across domains. INDEX TERMS Cross-domain recommendation, convolutional neural networks, rating prediction. NANNAN ZHENG received the Bachelor of Engineering degree from Xiamen University, China, where she is currently pursuing the degree with the Department of Automation. Her current research interests are in deep learning and recommendation systems at the System and Control Center Laboratory, Xiamen University. ZIANG XIONG received the Bachelor of Engineering degree from Xiamen University, China, in 2018, where he is currently pursuing the degree with the Department of Automation. His current research interests are in deep learning and recommendation systems at the System and Control Center Laboratory, Xiamen University. ZHIQIANG HU received the Bachelor of Engineering degree from the Chongqing University of Posts and Telecommunications, China. He is currently pursuing the degree with the Department of Automation, Xiamen University, China. His current research interests are in natural language processing and knowledge graph at the System and
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