Making Explainable Friend Recommendations Based on Concept Similarity Measurements via a Knowledge Graph

Abstract

Studying the similarities between the concepts in a knowledge graph can be useful in making friend recommendations on various microblogging platforms. Most existing approaches that only focus on accurate friend recommendation and can not give a reasoning explaining. In addition, existing similarity measurements are too costly and ineffective to be used in practical applications. To solve these problems, we purposed the shortest path-guide reasoning path, we perform explicit reasoning with knowledge for decision making so that the friend recommendation are supported by an interpretable causal inference. Then we designed a novel Weighted Euclidean-Shortest Path (WESP) method for measuring concept similarity in a knowledge graph and applied it to friend recommendations on a microblogging platform. First we took the shortest path as an example to measure concepts similarity. Although it was easy to use the shortest path to measure the similarity between concept pairs, the results of the measurements via the shortest path were affected by local structural imbalance in the knowledge graph. The imbalance had a significant impact on measuring concepts similarity; the more balanced the local structure, the greater similarity between the concept pairs. Then, we applied the WESP method to friend recommendations on the microblogging platform. We use the optimization similarity measurement (OSM) model that calculated the similarity between corresponding concept pairs. Our experimental results showed that the OSM method achieved better performance than the baseline methods in making friend recommendations.

Keywords

• Knowledge graph
• similarity measurement
• semantic similarity
• the shortest path
• Euclidean-shortest path
• imbalance structure