L
Longtao Huang
Researcher at Chinese Academy of Sciences
Publications - 42
Citations - 1418
Longtao Huang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Mobile computing. The author has an hindex of 15, co-authored 30 publications receiving 1045 citations. Previous affiliations of Longtao Huang include Massachusetts Institute of Technology & Alibaba Group.
Papers
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Journal ArticleDOI
On Deep Learning for Trust-Aware Recommendations in Social Networks
TL;DR: A two-phase recommendation process is proposed to utilize deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user’s trusted friendships.
Journal ArticleDOI
Computation Offloading for Service Workflow in Mobile Cloud Computing
TL;DR: A novel offloading system to design robust offloading decisions for mobile services is proposed and its approach considers the dependency relations among component services and aims to optimize execution time and energy consumption of executing mobile services.
Journal ArticleDOI
Social network-based service recommendation with trust enhancement
TL;DR: A social network-based service recommendation method with trust enhancement known as RelevantTrustWalker, utilizing a matrix factorization method to assess the degree of trust between users in social network and an extended random walk algorithm to obtain recommendation results.
Journal ArticleDOI
Mobility-Aware Service Composition in Mobile Communities
TL;DR: A mobile service provisioning architecture named a mobile service sharing community is proposed and a service composition approach by utilizing the Krill-Herd algorithm is proposed, which can obtain superior solutions as compared with current standard composition methods in mobile environments.
Proceedings ArticleDOI
SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction
TL;DR: This paper proposes an end-to-end method to solve the task of Pair-wise Aspect and Opinion Terms Extraction (PAOTE), and treats the problem from a perspective of joint term and relation extraction rather than under the sequence tagging formulation performed in most prior works.