Collaborative filtering in directed graph

Abstract

Embodiments are disclosed for data computation of collaborative filtering in a social network. Collaborative filtering involves predicting a user's behavior or interests based on other users' behavior or interests. To predict a user's interests in an item such as a picture, a system performs an iterative computation to perform an evaluation by solving an objective function. The system characterizes 'users' as 'vertices' in a directed graph, “relationship among users” as “edges” in the directed graph, and “items” as “worker data” that is locally-calculated, stored, and managed in individual worker computers. When a local computing process is completed, the “worker data” can be transferred to other worker computers so as to complete a whole computing process. The system enhances an overall computing efficiency and enables collaborative filtering across a large data set.