Dynamic QoS Prediction via a Non-Negative Tensor Snowflake Factorization
This addresses the challenge of incomplete QoS data affecting service selection for users in Web services, representing an incremental improvement in tensor factorization methods.
The paper tackles the problem of predicting unobserved quality of service (QoS) data in dynamic user-service interactions by proposing a Non-negative Snowflake Factorization of tensors model, which more accurately learns temporal patterns and yields improved predictions for missing data.
Dynamic quality of service (QoS) data exhibit rich temporal patterns in user-service interactions, which are crucial for a comprehensive understanding of user behavior and service conditions in Web service. As the number of users and services increases, there is a large amount of unobserved QoS data, which significantly affects users'choice of services. To predict unobserved QoS data, we propose a Non-negative Snowflake Factorization of tensors model. This method designs a snowflake core tensor to enhance the model's learning capability. Additionally, it employs a single latent factor-based, nonnegative multiplication update on tensor (SLF-NMUT) for parameter learning. Empirical results demonstrate that the proposed model more accurately learns dynamic user-service interaction patterns, thereby yielding improved predictions for missing QoS data.