TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features

• URL: http://arxiv.org/abs/2303.18201v2
• Date: Sat, 14 Oct 2023 16:40:27 GMT
• Title: TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features
• Authors: Suraj Kumar, Soumi Chattopadhyay, Chandranath Adak
• Abstract summary: Temporal Prediction is essential to identify a suitable service over time. Recent methods hardly achieved desired accuracy due to various limitations. This paper proposes a scalable strategy for Temporal Prediction using Multi-source Collaborative-Features.
• Score: 0.5161531917413706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, with the rapid deployment of service APIs, personalized service recommendations have played a paramount role in the growth of the e-commerce industry. Quality-of-Service (QoS) parameters determining the service performance, often used for recommendation, fluctuate over time. Thus, the QoS prediction is essential to identify a suitable service among functionally equivalent services over time. The contemporary temporal QoS prediction methods hardly achieved the desired accuracy due to various limitations, such as the inability to handle data sparsity and outliers and capture higher-order temporal relationships among user-service interactions. Even though some recent recurrent neural-network-based architectures can model temporal relationships among QoS data, prediction accuracy degrades due to the absence of other features (e.g., collaborative features) to comprehend the relationship among the user-service interactions. This paper addresses the above challenges and proposes a scalable strategy for Temporal QoS Prediction using Multi-source Collaborative-Features (TPMCF), achieving high prediction accuracy and faster responsiveness. TPMCF combines the collaborative-features of users/services by exploiting user-service relationship with the spatio-temporal auto-extracted features by employing graph convolution and transformer encoder with multi-head self-attention. We validated our proposed method on WS-DREAM-2 datasets. Extensive experiments showed TPMCF outperformed major state-of-the-art approaches regarding prediction accuracy while ensuring high scalability and reasonably faster responsiveness.

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