Multi-Modal Reciprocal Spatiotemporal Framework for Predicting Usage Trend of Knowledge Services

As an emerging concept, Knowledge as a Service (KaaS) aims to provide on-demand content-based (data, information, knowledge) delivery to meet the needs of users. With the prosperity of knowledge services, the prediction of the usage tendency of knowledge services has become an important and timely research topic. This study focuses on speculating the possible popularity of knowledge services in the next period of time, which can assist other downstream service tasks such as service recommendations. The interactions among knowledge services and their rich information (such as historical usage observation and text information) provide grounding for predicting the usage trend of services. However, recent spatial-temporal prediction based on graph neural networks usually depends heavily on the quality of manually created graphs, which may be expensive for knowledge services. To tackle such a limitation, this article proposes a novel Multi-modal Reciprocal SpatioTemporal (MRST) framework, which can jointly mine spatial dependencies and model time patterns for spatiotemporal coupling prediction. Two types of Edge Inference Networks (called EIN-o and EIN-t) are designed to sufficiently discover the spatial dependencies among knowledge services based on the data of usage observation sequences and service descriptions, respectively, and generate multi-modal directed weighted knowledge service graphs. Based on these graphs, MRST integrates GCN-based spatiotemporal prediction models as backbones to make predictions. Particularly, MRST features a unique reciprocal framework. On the one hand, EINs infer and generate multi-modal graphs to serve GCNs; on the other hand, GCNs utilize such spatial dependencies to make predictions and then introduce feedback to optimize EINs. In the meantime, to facilitate reproducible research, we collect a new knowledge service dataset from Wikipedia called Wiki-EN dataset. Experiments on this real data set show that the proposed MRST framework significantly surpasses the baselines and can learn meaningful spatial dependencies outside the predefined graphic structure.