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arXiv · 2301.11009

Graph-based Recommendation for Sparse and Heterogeneous User Interactions

Simone Borg Bruun·Kacper Kenji Lesniak·Mirko Biasini·Vittorio Carmignani·Panagiotis Filianos·Christina Lioma·Maria Maistro
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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.

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Simone Borg Bruun, Kacper Kenji Lesniak, Mirko Biasini, Vittorio Carmignani, Panagiotis Filianos, Christina Lioma, Maria Maistro. 2023-01-26. Graph-based Recommendation for Sparse and Heterogeneous User Interactions. https://doi.org/10.1007/978-3-031-28244-7_12

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