Personalized Open API Recommendation in Clouds Via Item-based Collaborative Filtering
Huifeng Sun, Zibin Zheng, Junliang Chen, Weimin Pan, Chuanchang Liu, Wenming Ma · 2011
In recent years, there are more and more Open APIs available on the Internet that can be invoked by independent users for their innovative applications. With the development of cloud computing, the number of Open APIs in clouds is also increasing. With the number increasing of functionally-equivalent Open APIs in the cloud, optimal API selection is becoming more and more important. In this paper, we propose a user-group item-based collaborative filtering approach for personalized Open API recommendation in clouds. Our approach is inspired by the idea that item similarity can be measured based on users with similar preference. Our approach classifies similar users into a user-group, and employs item-based collaborative filtering within the user-group. Our approach has good scalability and prediction accuracy. Extensively experimental results based on real-world QoS values of Open APIs demonstrate the effectiveness of our approach.