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.
@article{arxiv.2301.11009,
title = {Graph-based Recommendation for Sparse and Heterogeneous User Interactions},
author = {Simone Borg Bruun and Kacper Kenji Lesniak and Mirko Biasini and Vittorio Carmignani and Panagiotis Filianos and Christina Lioma and Maria Maistro},
journal= {arXiv preprint arXiv:2301.11009},
year = {2024}
}