Chen Yang
Shenzhen University
28 Papers
59 Citations
Chen Yang is an academic researcher from Shenzhen University. The author has contributed to research in topics: Recommender system & Computer science. The author has an hindex of 9, co-authored 28 publications. Previous affiliations of Chen Yang include University of Science and Technology of China & City University of Hong Kong.
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Papers
Good drivers pay less: A study of usage-based vehicle insurance models
TL;DR: A Behavior-centric Vehicle Insurance Pricing model (BVIP) and a vehicle premium calculation prototype are developed and research results show that BVIP achieves better accuracy in terms of risk-level classification and the prototype achieves good performance in Terms of effectiveness and usability.
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A social recommendation system for academic collaboration in undergraduate research
TL;DR: Results have shown that the proposed social recommendation system can facilitate undergraduates' selection of research projects.
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Leveraging semantic features for recommendation: Sentence-level emotion analysis
TL;DR: Wang et al. as discussed by the authors proposed a hybrid personalized recommendation model that extracts user preferences by analyzing user review content in different sentiment polarity at the sentence level, based on jointly applying user-item score matrices and dimension reduction methods.
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Identifying expertise through semantic modeling: A modified BBPSO algorithm for the reviewer assignment problem
TL;DR: A novel optimization model with several review condition constraints to address the reviewer assignment problem is proposed and can help the managers to efficiently and effectively select reviewers in terms of the convergence rate and convergence level when compared with several classic benchmarks.
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HNRWalker: recommending academic collaborators with dynamic transition probabilities in heterogeneous networks
TL;DR: An improved random walk algorithm known as “Heterogeneous Network-based Random Walk” (HNRWalker) with dynamic transition probability and a new rule for selecting candidates are proposed and performs better than the benchmarks in improving recommendation performances.
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