Abstract
Service recommendation technology is the key to realize the personalization of intelligent services. The recommended services need to meet functional requirements as well as non-functional requirements. Therefore, QoS-based service recommendation came into being. To perform intelligent service recommendations, matching users with convenient services based on QoS becomes an inevitable task. However, most of the service recommendation models are based on user interaction records to predict and recommend, ignoring the service-user correlation and unstable QoS values. In this article, we propose a new service recommendation model. We have performed two-tier filtering calculation on a large number of Web Services, filtering the contextual information of users and services and the instability of services. In the first filtering layer, we take the instability of QoS as an indicator to eliminate invalid services, which significantly reduces the service scale and eliminates the interference of invalid services on the recommendation to a certain extent. Further, we process the contextual information of both users and services in the second filtering layer. Considering the impact of the correlation between the service and the user, we use the geographic location information of the user and the service, and solve the combined features generated by the similarity between the user and the service to filter. Considering the sparsity of the service recommendation environment and the influence of noise generated by useless features, we use a model of factorization machine combined with the attention mechanism for computational processing. It effectively distinguishes the interactive importance of different features. We have conducted many experiments on real dataset, and the results show that our model is better than most baseline model in terms of recommendation performance.
Highlights
"Service" exists in all aspects of our lives
We proposed a service filtering strategy to filter invalid services generated during user interaction
We find that Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) decrease when matrix density increases
Summary
"Service" exists in all aspects of our lives. Web services are becoming more and more popular among users, such as booking hotels and buying movie tickets. The number of historically invoked services by users is usually minimal, resulting in a severe lack of historical QoS values This situation is an urgent problem to be solved in the service computing environment. To perform an intelligent service recommendation, it is an inevitable task to perform high-precision service prediction based on the service QoS value. Based on 1 and 2, we proposed a two-tier filtered service recommendation model This model combines the motivation of filtering invalid services and location information. Through two filterings, it solves the problem of invalid service interference in the early stage, and solves the influence of context information and the relationship between service and user, and improves service recommendation efficiency. It solves the problem of invalid service interference in the early stage, and solves the influence of context information and the relationship between service and user, and improves service recommendation efficiency. We used the real Web service QoS dataset to carry on the experimental evaluation, the result verifies our proposed model has the good prediction ability
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