Graph-based Recommendation for Sparse and Heterogeneous User Interactions

TL;DR

This paper introduces a graph-based recommender system tailored for small-scale, heterogeneous user-content interactions, utilizing a genetic algorithm to optimize relationship weights, resulting in significant improvements over existing methods.

Contribution

The paper presents a novel graph-based recommendation model that effectively handles sparse, heterogeneous data in small datasets, with an optimization approach using genetic algorithms.

Findings

Up to 7% improvement over state-of-the-art methods

Statistically significant and consistent results across datasets

Effective on small-scale, heterogeneous interaction data

Abstract

Recommender system research has oftentimes focused on approaches that operate on large-scale datasets containing millions of user interactions. However, many small businesses struggle to apply state-of-the-art models due to their very limited availability of data. We propose a graph-based recommender model which utilizes heterogeneous interactions between users and content of different types and is able to operate well on small-scale datasets. A genetic algorithm is used to find optimal weights that represent the strength of the relationship between users and content. Experiments on two real-world datasets (which we make available to the research community) show promising results (up to 7% improvement), in comparison with other state-of-the-art methods for low-data environments. These improvements are statistically significant and consistent across different data samples.