Review Article

Trusted Service Evaluation for Mobile Edge Users: Challenges and Reviews

Table 1

Research protocol of subjective user rating.

CategoryTitleAuthorsYearPublisher

Feedback incentive of user ratingCommunity-Diversified Influence Maximization in Social Networks [16]Li et al.2020Information Systems
Hybrid Attacks on Model-Based Social Recommender Systems [32]Yu et al.2017Physica A: Statistical Mechanics & Its Applications

Identification and punishment of malicious ratingCommunity-Diversified Influence Maximization in Social Networks [16]Li et al.2020Information Systems
Rater Credibility Assessment in Web Services Interactions [21]Malik et al.2009World Wide Web Journal
Shilling Attack Detection in Recommender Systems via Selecting Patterns Analysis [33]Li et al.2016IEICE Transactions on Information and System
Identifying Fake Feedback for Effective Trust Management in Cloud Environments [34]Noor et al.2013LNCS
A. Krause. Incentive-Compatible Forecasting Competitions [35]Witkowski et al.2018Thirty-Second AAAI Conference on Artificial Intelligence
Study on the Trust Evaluation Approach Based on Cloud Model [36]Zhang et al.2013Chinese Journal of Computers

Identification and correction of preference ratingsCommunity-Diversified Influence Maximization in Social Networks [16]Li et al.2020Information Systems
Rater Credibility Assessment in Web Services Interactions [21]Malik et al.2009World Wide Web Journal
Reputation Measurement and Malicious Feedback Rating Prevention in Web Service Recommendation System [31]Wang et al.2015IEEE Transactions on Services Computing

Weight allocation of user ratingsA Time-Aware Dynamic Service Quality Prediction Approach for Services [7]Jin et al.2020Tsinghua Science and Technology
An Attention-Based Category-Aware GRU Model for Next POI Recommendation [15]Liu et al.2021International Journal of Intelligent Systems