The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric
The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric
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胆固醇因素：通过特定营养指标平衡食谱推荐的准确性和健康状况
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Vegard Solberg
中科院分区：
文献类型：
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作者：
A. Starke;C. Trattner;Hedda Bakken;Martin Johannessen;Vegard Solberg
尽管许多食物推荐系统针对用户的偏好进行了优化，但健康是另一个经常被忽视的目标。本文旨在推荐相关食谱，避免导致高胆固醇水平的营养素，如饱和脂肪和糖。我们引入了一种名为“胆固醇因子”的新指标，该指标基于挪威卫生局的营养指南，可以通过后过滤中的线性重新加权来平衡准确性和健康。我们通过评估来自AllRecipes.com的食谱数据集来测试流行的推荐方法，其中基于CF的SVD方法优于基于内容的方法和混合方法。虽然我们发现，提高推荐食谱集的健康程度是以牺牲精确度和召回率指标为代价的，但只需对胆固醇因子施加很少的权重（10-15%），就可以在准确率损失最小的情况下显著提高推荐集的健康程度。
Whereas many food recommender systems optimize for users’ preferences, health is another but often overlooked objective. This paper aims to recommend relevant recipes that avoid nutrients that contribute to high levels of cholesterol, such as saturated fat and sugar. We introduce a novel metric called ‘The Cholesterol Factor’, based on nutritional guidelines from the Norwegian Directorate of Health, that can balance accuracy and health through linear re-weighting in post-filtering. We tested popular recommender approaches by evaluating a recipe dataset from AllRecipes.com, in which a CF-based SVD method outperformed content-based and hybrid methods. Although we found that increasing the healthiness of a recommended recipe set came at the cost of Precision and Recall metrics, only putting little weight (10-15%) on our Cholesterol Factor can significantly improve the healthiness of a recommendation set with minimal accuracy losses.
DOI：
10.1145/2959100.2959189
发表时间：
2016-09
期刊：
Proceedings of the 10th ACM Conference on Recommender Systems
影响因子：
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作者：
Bart P. Knijnenburg;S. Sivakumar;Daricia Wilkinson
通讯作者：
Bart P. Knijnenburg;S. Sivakumar;Daricia Wilkinson