Bridge the gap between network-based inference method and global ranking method in personal recommendation

TL;DR

This paper establishes a theoretical link between network-based inference and global ranking methods in personal recommendation, showing that the latter is the limit of the former as iterations approach infinity.

Contribution

It provides a theoretical proof connecting network-based inference with global ranking, clarifying their relationship in recommendation systems.

Findings

Global ranking is the limit of network-based inference with infinite iterations.

Theoretical analysis bridges two common recommendation approaches.

Provides insights for designing more effective recommendation algorithms.

Abstract

In this paper, we study the relationship between the network-based inference method and global ranking method in personal recommendation. By some theoretical analysis, we prove that the recommendation result under the global ranking method is the limit of applying network-based inference method with infinity times.