Q
Qilin Wu
Researcher at Chaohu University
Publications - 5
Citations - 120
Qilin Wu is an academic researcher from Chaohu University. The author has contributed to research in topics: Computer science & Embedding. The author has an hindex of 1, co-authored 1 publications receiving 57 citations.
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Journal ArticleDOI
Location-Aware Deep Collaborative Filtering for Service Recommendation
TL;DR: A new deep CF model for service recommendation, named location-aware deep CF (LDCF), which can not only learn the high-dimensional and nonlinear interactions between users and services but also significantly alleviate the data sparsity problem.
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GAP-MM: 5G-Enabled Real-Time Autonomous Vehicle Platoon Membership Management Based on Blockchain
Bin Wu,Qilin Wu,Zuobin Ying +2 more
TL;DR: A membership management scheme for 5G-enabled RAVP is proposed by integrating revocable attribute-based encryption (RABE) and blockchain, namely, GAP-MM that realizes fine-grained access control of key distribution and malicious vehicle’s key revocation efficiently.
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A Dynamic Heterogeneous Information Network Embedding Method Based on Meta-Path and Improved Rotate Model
TL;DR: Wang et al. as discussed by the authors proposed a dynamic heterogeneous information network embedding method based on the meta-path and improved Rotate model; this method first uses metapaths to model the semantic relationships involved in the heterogenous information network, then uses GCNs to get local node embedding, and finally uses meta path-level aggression mechanisms to aggregate local representations of nodes.
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Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction
TL;DR: This work proposes a novel relationship discovery and hierarchical embedding method based on GCNs (named as RDHE), which designs a dual mechanism to represent services and users, respectively, and designs a new community discovery method and a fast similarity calculation process, which can fully mine and utilize the relationships in the graph.
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Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration
TL;DR: Wang et al. as discussed by the authors proposed the Hybrid AdaBoost Ensemble Method (HABEM), which uses the membership function and cluster center selection in fuzzy clustering to calculate the scoring matrix of the user-items, then the clustering user items' scoring matrix is trained by the neural network to improve the scoring prediction accuracy further.