A Hierarchical Optimizer for Recommendation System Based on Shortest Path Algorithm

TL;DR

This paper introduces a hierarchical optimizer for recommendation systems that enhances shortest path algorithms with classifiers and a constant optimizer to improve recommendation quality based on service provider features.

Contribution

It proposes a novel hierarchical optimizer integrating classifiers and a constant optimizer to improve geosocial keyword query recommendations.

Findings

Enhanced recommendation accuracy through hierarchical optimization

Effective integration of classifiers with shortest path algorithms

Improved relevance of recommendations based on provider features

Abstract

Top-k Nearest Geosocial Keyword (T-kNGK) query on geosocial network is defined to give users k recommendations based on some keywords and designated spatial range, and can be realized by shortest path algorithms. However, shortest path algorithm cannot provide convincing recommendations, so we design a hierarchical optimizer consisting of classifiers and a constant optimizer to optimize the result by some features of the service providers.