Graph-based Recommendation for Sparse and Heterogeneous User Interactions

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 recommende...

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Bibliographic Details
Published inarXiv.org
Main Authors Simone Borg Bruun, Kacper Kenji Lesniak, Biasini, Mirko, Carmignani, Vittorio, Filianos, Panagiotis, Lioma, Christina, Maistro, Maria
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 26.01.2023
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Computer Science - Information Retrieval
Datasets
Genetic algorithms
Recommender systems
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2301.11009