This repository is the Python implementation for the SCC 2020 accepted paper:

Guosheng Kang, Jianxun Liu, Buqing Cao, Yong Xiao. "Diversified QoS-Centric Service Recommendation for Uncertain QoS Preferences". IEEE International Conference on Services Computing. 2020, pp. 288-295.

Procedure of DiQoS

Dataset

The experiments were conducted on a widely used public real-world dataset named QWS [1]. This dataset can be accessed from Zenodo website. The dataset contains 8-dimensional quality information on 2,507 real-world Web services, including latency, availability, etc.
[1] E. Al-Masri, and Q. H. Mahmoud, “Investigating Web Services on the World Wide Web,” International Conference on World Wide Web, 2008, pp. 795-804.

Data preprocessing

• Code: data_preprocess.py
• Input: qws2/qws2.csv
• Output: QWS.pickle

Approaches

Comparing Approaches

We have implemented DiQoS and other four existing representative approaches.

• DSL-RS: This baseline approach randomly selects k services from S_DSL.
• DSL-KNN [2]: This approach models services recommendation as a k nearest neighbors problem. It selects k services from S_DSL that are most similar to s_r. This is the first attempt to solve the problem of personalized quality centric service recommendation.
• DQCSR-CC and DQCSR-CR [3]: These approaches first identify the S_DSL. Then the identified services are clustered with K-Means algorithm. DQCSR-CC selects a service from each cluster which is nearest to its cluster center, and DQCSR-CR selects a service from each cluster whose coverage region has the minimum radius. This is the first attempt to handle users’ uncertain quality correlation in service recommendation.

[2] Y. Zhang, X. Ai, Q. He, X. Zhang, W. Dou, F. Chen, L. Chen, and Y. Yang, “Personalized Quality Centric Service Recommendation,” International Conference on Service-Oriented Computing, 2017, pp. 528-544.
[3] Y. Zhang, L. Wu, Q. He, F. Chen, S. Deng, and Y. Yang, “Diversified Quality Centric Service Recommendation,” IEEE International Conference on Web Services, 2019, pp. 126-133.

Implementation

• Code: approaches.py

Evaluation

Evaluate Effectiveness

• Code: approaches.py
• Input: QWS.pickle
• Output: DCG_value_n.csv, Div_value_n.csv, DCG_value_d.csv, Div_value_d.csv, DCG_value_k.csv, Div_value_k.csv

Evaluate Efficiency

• Code: evaluate_efficiency.py
• Output: running_time_n.csv, running_time_d.csv, running_time_k.csv

Plot Figures for Effectiveness and Efficiency

• Python Code: plot_effectiveness.py, plot_efficiency.py
• Output: effectiveness.png, efficiency.png
• Matlab Code: plot_effectiveness.m, plot_efficiency.m
• Output: effectiveness.emf, efficiency.emf

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