GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation

TL;DR

GHRS is a graph-based hybrid recommendation system that combines user ratings, demographics, and location data with autoencoder features to improve accuracy, especially in cold-start scenarios, demonstrated on MovieLens data.

Contribution

The paper introduces GHRS, a novel hybrid recommender system utilizing graph models and autoencoder features to enhance recommendation accuracy and cold-start performance.

Findings

GHRS outperforms existing algorithms on MovieLens dataset.

Autoencoder-based feature extraction improves user clustering.

Hybrid approach effectively addresses cold-start problem.

Abstract

Research about recommender systems emerges over the last decade and comprises valuable services to increase different companies' revenue. Several approaches exist in handling paper recommender systems. While most existing recommender systems rely either on a content-based approach or a collaborative approach, there are hybrid approaches that can improve recommendation accuracy using a combination of both approaches. Even though many algorithms are proposed using such methods, it is still necessary for further improvement. In this paper, we propose a recommender system method using a graph-based model associated with the similarity of users' ratings, in combination with users' demographic and location information. By utilizing the advantages of Autoencoder feature extraction, we extract new features based on all combined attributes. Using the new set of features for clustering users, our proposed approach (GHRS) has gained a significant improvement, which dominates other methods' performance in the cold-start problem. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms many existing recommendation algorithms on recommendation accuracy.