An Adaptively Bias-Extended Non-Negative Latent Factorization of Tensors Model for Accurately Representing the Dynamic QoS Data

Time-varying quality-of-service (QoS) data are usually utilized for Web service evaluation and selection. To accurately estimate the unknown information in time-varying QoS data, it is crucial to capture the temporal patterns hidden in the known data. The Non-negative Latent Factorization of Tensors (NLFT) model has performed well in describing the temporal patterns in time-varying QoS data. However, it assigns a single bias to each dimension of the target QoS tensor, making it suffer from estimation accuracy loss when describing the fluctuations of time-varying QoS data. To address this vital issue, this paper proposes an Adaptively Bias-extended NLFT (ABNT) model based on the fuzzy logic with two-fold ideas: a) extending the linear biases on each dimension of tensor for describing the complex fluctuations of QoS data precisely, b) building a fuzzy logic-incorporated particle swarm optimization algorithm to establish a self-adaptation mechanism for the count of extended linear biases and regularization coefficients. Detailed algorithms and analyses are provided for the proposed ABNT model. Empirical studies on two practical time-varying QoS datasets indicate that the estimation accuracy of the ABNT model outperforms that of state-of-the-art QoS data estimation models (with an average 23.94% improvement in MAE).