Collaborating Filtering Method Based on Multiple Data Sources

Abstract

The highly efficient utilization of multiple data sources is a key challenge in big data applications.Based on the collaborative filtering recommendation，services pick consumption behaviors of similar clients by clustering to generate the recommendation list.Client clustering contains two units，one is preliminary clustering，and the other is synthetic clustering.Preliminary clustering use client-product score matrixes，telecommunication service identities，client network behaviors and etc.to calculate similarities.Synthetic clustering weights the abundance of data，and then completes the similarity calculation and client clustering.Adjustable weights of data validity were introduced to optimize the system on the basis of click rates and conversion rates of recommendation list.

Keywords

• recommendation method
• data fusion
• telecom corporation
• weight