Integrating meta-path selection with user-preference for top-k relevant search in heterogeneous information networks

Abstract

Relevance search in heterogeneous information networks is a basic and crucial operation which is usually used in recommendation, clustering and anomaly detection. Nowadays most existing relevance search methods focus on objects in homogeneous information networks. In this paper, we propose a method to find the top-k most relevant objects to a specific one in heterogeneous networks. It is a two phase process that we get the initial relevance score based on the method of pair wise random walk along given meta-paths, which is a meta-level description of the path instances in heterogeneous information networks, and then take user preference into consideration to calculate the weights combination of meta-paths and model the problem into a multi-objective linear planning problem which can be solved with the method of generic algorithm. Besides, to ensure the efficiency, we use matrix computation and selective materialization to avoid the recursive computation of pair wise random walk. What's more, we propose an effective pruning method to skip unnecessary objects computations. The experiments on IMDB and DBLP dataset show that the method can gain a better accuracy and efficiency.