Towards a transfer learning approach to food recommendations through food images

Hasan, Hafiz Muhammad Zubair; Khan, Hassan; Asif, T Chinwalla; Hashmi, Salman; Rafi, Muhammad

doi:10.1145/3310986.3310990

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…For this purpose, three levels of quality have been defined: High (H) the articles with a score between 8 and 11, Medium (M) between 5.1 and 7.9, and Low (L) less than 5. After the qualification, we can affirm that within the metrics used in this study, the articles [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] are of high impact.…”

Section: Resultsmentioning

confidence: 71%

Systematic Review of Nutritional Recommendation Systems

2021

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In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. There are many apps created to count calories based on what we eat, or to estimate calorie consumption according to the sport we do, or to recommend recipes, but very few are capable of giving personalized recommendations. This review tries to see what studies exist and what recommendation systems are used for this purpose, over the last 5 years in the main databases. Among the results obtained, it is observed that the existing works focus on the recommendation system (usually collaborative filtering), and not so much on the description of the data or the sample analyzed; the indices used for the calculation of calories or nutrients are not specified. Therefore, it is necessary to work with open data, or well-described data, which allows the experience to be reproduced by third parties, or at least to be comparable. In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society.

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Section: Resultsmentioning

confidence: 71%

Systematic Review of Nutritional Recommendation Systems

2021

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“…There was demonstration that digital imaging could estimate food information in many environments and it had many advantages over other methods [4][5]. However, to derive the food information such as food type, food combination and portion size from food images remains uncertainty.…”

Section: Introductionmentioning

confidence: 99%

A Food Recommender Based on Frequent Sets of Food Mining Using Image Recognition

Tangpong¹,

Leanghirun²,

Hansuebsai³

et al. 2021

Artificial Intelligence

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Food recommendation is an important service in our life. To set a system, we searched a set of food images from social network which were shared or reviewed on the web, including the information that people actually chose in daily life. In the field of representation learning, we proposed a scalable architecture for integrating different deep neural networks (DNNs) with a reliability score of DNN. This allowed the integrated DNN to select a suitable recognition result obtained from the different DNNs that were independently constructed. The frequent set of foods extracted from food images was applied to Apriori data mining algorithm for the food recommendation process. In this study, we evaluated the feasibility of our proposed method.

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