Reputation-aware QoS Value Prediction of Web Services Weiwei Qiu, Zhejiang University Zibin Zheng, The Chinese University of HongKong Xinyu Wang, Zhejiang University Xiaohu Yang, Zhejiang University Michael R. Lyu, The Chinese University of HongKong

Outline Problem Background Related Work Prediction Framework Overview Reputation-aware QoS value Prediction Experiments Conclusion Future work 2

Background Web Service QoS value prediction is an important research issue for service recommendation, selection and composition. Historical data contributed by users can have great impacts on prediction results. Existing Web service QoS prediction approaches did not take data credibility into consideration. 3

Aim Take user trustworthiness into account to make more accurate QoS value prediction. 4

Reputation-Aware Prediction (RAP) User reputation ranking ▫Calculates the reputation for each user based on the historical evaluation data ▫Identify untrustworthy users by reputation ranking Neighborhood-based collaborative filtering method for QoS value prediction ▫User-based and item-based prediction 5

Related work Collaborative Filtering ▫Memory -based vs. Model-based approaches ▫User-based, item-based and hybrid methods ▫Enhancement methods, such as: RegionKNN, Location-Aware CF etc. 6

Reputation Systems ▫Compute and publish reputation scores for entities based on ratings and feedbacks ▫Simple summation, average of ratings, Bayesian systems, Discrete Trust Models ▫Co-determination algorithm 7

Prediction Framework 8

Prediction Process 9

User Reputation Calculation Basic points: ▫Use the difference between current user’s ratings and the corresponding services’ aggregated ratings of other users to measure the user reputation ▫Users with ratings which often have great difference with others will have low reputation values. 10

Untrustworthy User Identification Top-R users who have lower reputation values than others, will be identified as untrustworthy users Problem: the value of Top-R. 11

Reputation-aware Collaborative Filtering Algorithm Similarity calculation ▫Pearson Correlation Coefficient (PCC)  User similarity:  Service similarity: 12

Hybrid CF approach: User-based Item-based: 13

Experiments Experimental setup ▫Dataset: 339*5825 user-service matrix (WSDream dataset2) ▫Compare with:  UPCC  IPCC  UIPCC Metrics ▫MAE ▫RMSE 14

Experiments 15 Performance comparison

16 Impact of Top-R

Impact of factor d 17

Impact of λ 18

Conclusion In this paper, we propose an reputation-aware QoS value prediction approach (RAP) of Web Services. The experimental results show that RAP solves the data credibility problem of CF neighborhood-based methods and has significant prediction accuracy improvement. 19

Future work Remove the parameter Top-R ▫Reduce the weight of low reputation users Cluster the users before reputation calculation ▫Location information 20

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