Toward Knowledge as a Service (KaaS): Predicting Popularity of Knowledge Services Leveraging Graph Neural Networks

Abstract

Knowledge services are becoming a rising star in the family of XaaS (Everything as a Service). In recent years, people are more willing to search for answers and share their knowledge directly over the Internet, which makes the knowledge service ecosystem prosperous. In this paper, we aim to predict the popularity of knowledge services, which will benefit the downstream industries. Toward such a task, the spatial interactions (e.g., hyperlinks in Wikipedia) and temporal observations (e.g., page views) provide crucial information. However, it is difficult to utilize this information due to: (i) complicated and different usage observations, (ii) intricate and evolutionary spatial interactions, and (iii) small world trait of the network. To tackle such issues, we propose evolutionary graph convolutional recurrent neural networks (E-GCRNNs) to simultaneously model both temporal and spatial dependencies of knowledge services from their evolving networks. Additionally, a localized mini-batch training scheme is developed, which allows the E-GCRNNs to work on large-scale knowledge services network and reduce the prediction bias caused by the small world trait. Extensive experiments on real-world datasets have demonstrated that the proposed E-GCRNNs outperform baselines in terms of prediction accuracy, especially with the prediction range being longer, while remaining computationally efficient.