QoS Prediction Method via Multi-Task Learning for Web Service Recommendation
Jinyu Wang, Xing Zhang, Qi Wang, Wenguang Zheng, Yingyuan Xiao · 2024
With the increasing number of web services on the Internet, it has become an important challenge to recommend the optimal service that meets the needs of users. Quality of Service (QoS) plays an important role in service recommendation. Most existing methods ignore the correlation information among QoS attributes, and their prediction accuracy can be further improved. We propose Weight Adaptive Multi-Task Learning method(WAMTL). WAMTL treats the prediction process of a single QoS attribute as a sub-task, simultaneously predicting the values of multiple QoS attributes in parallel. In parallel learning, by making full use of the correlation information among QoS attributes, the prediction process of QoS attributes promotes each other and improves the accuracy of the prediction results. By introducing adaptive sub-task loss weight into the model, the problem of unbalanced learning rate of sub-tasks in multi- task learning method is alleviated. We conducted comprehensive experiments on real observed datasets. The experimental results show that our method significantly outperforms the comparison models in terms of prediction accuracy.