Evaluating Generative AI Tools for Personalized Offline Recommendations: A Comparative Study
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908479662325760 |
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| author | Salinas-Buestan, Rafael Parra, Otto Condori-Fernandez, Nelly Granda, Maria Fernanda |
| author_facet | Salinas-Buestan, Rafael Parra, Otto Condori-Fernandez, Nelly Granda, Maria Fernanda |
| contents | Background: Generative AI tools have become increasingly relevant in supporting personalized recommendations across various domains. However, their effectiveness in health-related behavioral interventions, especially those aiming to reduce the use of technology, remains underexplored. Aims: This study evaluates the performance and user satisfaction of the five most widely used generative AI tools when recommending non-digital activities tailored to individuals at risk of repetitive strain injury. Method: Following the Goal/Question/Metric (GQM) paradigm, this proposed experiment involves generative AI tools that suggest offline activities based on predefined user profiles and intervention scenarios. The evaluation is focused on quantitative performance (precision, recall, F1-score and MCC-score) and qualitative aspects (user satisfaction and perceived recommendation relevance). Two research questions were defined: RQ1 assessed which tool delivers the most accurate recommendations, and RQ2 evaluated how tool choice influences user satisfaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03710 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Generative AI Tools for Personalized Offline Recommendations: A Comparative Study Salinas-Buestan, Rafael Parra, Otto Condori-Fernandez, Nelly Granda, Maria Fernanda Information Retrieval Machine Learning Background: Generative AI tools have become increasingly relevant in supporting personalized recommendations across various domains. However, their effectiveness in health-related behavioral interventions, especially those aiming to reduce the use of technology, remains underexplored. Aims: This study evaluates the performance and user satisfaction of the five most widely used generative AI tools when recommending non-digital activities tailored to individuals at risk of repetitive strain injury. Method: Following the Goal/Question/Metric (GQM) paradigm, this proposed experiment involves generative AI tools that suggest offline activities based on predefined user profiles and intervention scenarios. The evaluation is focused on quantitative performance (precision, recall, F1-score and MCC-score) and qualitative aspects (user satisfaction and perceived recommendation relevance). Two research questions were defined: RQ1 assessed which tool delivers the most accurate recommendations, and RQ2 evaluated how tool choice influences user satisfaction. |
| title | Evaluating Generative AI Tools for Personalized Offline Recommendations: A Comparative Study |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2508.03710 |