期刊
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
卷 37, 期 7, 页码 1656-1665出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2019.2916452
关键词
QoE; network media; privacy-aware users; user classification; data sparsity; unreliable data
资金
- National Natural Science Foundation of China [61571240, 61671474]
- ZTE Program The Prediction of Wireline Network Malfunction and Traffic based on Big Data
- Priority Academic Program Development of Jiangsu Higher Education Institutions
More and more network media users concern about their privacy issues since they know that their network behaviors are being observed, and thus the observable users' data are not reliable and sufficient in this case. How to evaluate the true quality of experience (QoE) of the privacy-aware users has become a significant technical challenge because of the most majority of existing data-driven QoE evaluation schemes based on the premise of the true and adequate users' observations. To get over this dilemma, this paper proposes a systematic and robust QoE evaluation scheme with unreliable and insufficient observation data. Specifically, we first translate the subjective privacy-aware QoE evaluation problem into an objective rational user analysis procedure. Then, a semantics-based similarity measurement for multidimensional correlation analysis is constructed to classify the observable data. Subsequently, the highlight of this paper lies in proposing a class-level joint user classification and data cleaning strategy by frequently updating the training processes. Through elaborately designing an iterative framework, it can effectively resolve the data sparsity and inconsistency problems due to the user privacy-aware preferences. Importantly, we also introduce an efficient QoE model construction method for online implementation, and numerical results validate its efficiency for different kinds of privacy aware users.
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