A Hessian-Free Optimization-Based Approach to Latent-Factor-Based QoS Predictors with High Accuracy

Latent-factor-based Quality-of-Service predictors can achieve high prediction accuracy and good scalability. However, most of them are based on first-order models that cannot well deal with their target problem that is inherently non-convex. Since second-order approaches have proven to be effective to such problems, this work proposes to implement a second-order predictor with an aim to achieve the high accuracy unlikely obtained by any existing methods. To do so, this work adopts the principle of Hessian-free optimization and successfully avoids the usage of a Hessian matrix by employing the efficiently obtainable product between its Gauss-Newton approximation and an arbitrary vector. Experimental results on two industrial QoS datasets indicate that the newly proposed predictor is highly accurate with fine computational efficiency.