Abstract:
Since similar users use similar services, a latent factor (LF)-based QoS predictor needs neighborhood regularization. Neighborhood information is used in neighborhood-regularized LF models.
The former has low prediction accuracy due to the difficulty of constructing the neighborhood based on incomplete QoS data, while the latter requires additional geographical information that is difficult to collect due to information security, identity privacy, and commercial interests in real-world scenarios.
This study proposes a posterior-neighborhood-regularized LF (PLF) model for QoS prediction to address these issues. The main idea is to decompose the LF analysis process into three phases: a) primal LF extraction, where LFs are extracted to represent involved users/services based on known QoS data, b) posterior-neighborhood construction, where the neighborhood of each user/service is achieved based on similarities between their primal LF vectors, and c) posterior-neighborhood-regularized LF analysis, where the objective function is regularized by both the posterior-neighborhood PLF outperforms state-of-the-art models in accuracy and efficiency in large-scale QoS datasets.
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