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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2301.11009 |
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| _version_ | 1866911786862641152 |
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| author | Bruun, Simone Borg Lesniak, Kacper Kenji Biasini, Mirko Carmignani, Vittorio Filianos, Panagiotis Lioma, Christina Maistro, Maria |
| author_facet | Bruun, Simone Borg Lesniak, Kacper Kenji Biasini, Mirko Carmignani, Vittorio Filianos, Panagiotis Lioma, Christina Maistro, Maria |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_11009 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Graph-based Recommendation for Sparse and Heterogeneous User Interactions Bruun, Simone Borg Lesniak, Kacper Kenji Biasini, Mirko Carmignani, Vittorio Filianos, Panagiotis Lioma, Christina Maistro, Maria Information Retrieval 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. |
| title | Graph-based Recommendation for Sparse and Heterogeneous User Interactions |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2301.11009 |