The demand for a food recommendation service for dogs has rapidly increased with the increasing number of pet owners, because it is generally difficult for dog owners to find food that is perfectly suitable for their dogs' health condition. The purpose of this study is to develop an algorithm for recommending dog food that contains appropriate nutrients based on the physical and health conditions of the dogs. This study proposes a nutrient profiling‐based recommendation algorithm (NRA) for dog food. The proposed algorithm tries to recommend appropriate or inappropriate dog food by using collective intelligence based on user experience and the prior knowledge of experts. Based on the physical and health status of dogs, this study extracts which nutrients are necessary for the dogs and recommends the most suitable dog food containing these nutrients. A performance evaluation was implemented in terms of recall, precision, F1 and AUC. As a result of the performance evaluation, the AUC performance of this NRA is 20% higher than k‐NN and 9.7% higher than the SVD model. In addition, the NRA proved to be an evolving system in which the performance of recommendations improves as users' feedback accumulates.

中文翻译：

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基于营养适用性的狗食推荐系统

随着宠物主人的数量增加，对狗的食物推荐服务的需求迅速增加，因为狗主人通常很难找到完全适合其狗的健康状况的食物。这项研究的目的是根据狗的身体和健康状况，开发一种推荐含有适当营养成分的狗粮的算法。这项研究提出了一种基于营养成分分析的狗粮推荐算法（NRA）。提出的算法尝试根据用户体验和专家的先验知识，通过使用集体智慧来推荐合适或不适当的狗粮。根据狗的身体和健康状况，这项研究提取了狗必需的营养物质，并推荐了最合适的含有这些营养物质的狗粮。根据召回率，精度，F1和AUC进行了绩效评估。性能评估的结果是，此NRA的AUC性能比k‐NN高20％，比SVD模型高9.7％。此外，NRA被证明是一个不断发展的系统，在该系统中，随着用户反馈的累积，推荐的性能也会提高。