A Multi-Task Service Recommendation Model Considering Dynamic and Static QoS
Mingyu Li, Qin Lu, Mingge Zhang, Xinmei Liang · 2019
Recommending the best Web service for users is a necessary task in the internet environment. At present, many of the proposed recommendations have yielded good results. Among them, recommendation methods based on Quality of Service (QoS) emerge endlessly. However, most service recommendation methods consider static or dynamic QoS separately and do not fully consider the impact of their combination. In this paper, we proposed a multi-task service recommendation model that not only models high-order and low-order features simultaneously but also considers the context information generated by the user invoking the service. Our model integrates Factorization Machine (FM) and Bi-directional Long Short-Term Memory (Bi-LSTM) into a deep neural network structure, leveraging their feature combination and deep mining capabilities. Furthermore, we use a pair of attention mechanisms to focus the task model on finding useful information related to the current output in the service data to improve the results of service recommendations. We have done enormous experiments on real QoS data sets, and the results prove that compared with other mainstream recommendation methods, the recommendation performance of this method is greatly improved.