Session-based social and dependency-aware software recommendation

Article Computer Science, Information Systems

FunkR-pDAE: Personalized Project Recommendation Using Deep Learning

Pengcheng Zhang et al.

Summary: This paper proposes a novel personalized project recommendation approach called FunkR-pDAE, which first extracts data from developers and open source projects in open source communities, builds relevance matrices, calculates developer similarity using Pearson Correlation Coefficient, uses deep auto-encoders to learn factor vectors, and defines a sorting method to provide personalized project recommendations. Experimental results on real-world GitHub datasets show that FunkR-pDAE outperforms state-of-the-art approaches with a precision rate of 75.46 percent and a recall rate of 40.32 percent.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING (2021)

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Article Computer Science, Theory & Methods

Privacy-preserving quality prediction for edge-based IoT services

Yiwen Zhang et al.

Summary: The DEQP2 model is a distributed edge QoS prediction model with privacy protection, employing the Laplace vector mechanism and distributed edge differential privacy algorithm. Experiments conducted on the EdgeQoS dataset showed that the DEQP2 model provides measurable privacy preservation without significantly reducing accuracy.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2021)

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Article Computer Science, Information Systems