Real-time mobile recipe recommendation system using food ingredient recognition

Maruyama, Takuma; Kawano, Yoshiyuki; Yanai, Keiji

doi:10.1145/2390821.2390830

Cited by 34 publications

(21 citation statements)

References 7 publications

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“…Visual food analysis can obtain a high-level understanding of the type (e.g.,the food category and ingredients), the amount of food consumed by the user and even the calorie, and thus is very essential for food recommendation. This category can broadly be divided into different types, such as food category recognition [14], food ingredient recognition [7], cooking instruction recognition [49] and food quantity estimation [32], [41]. However, accurate visual food analysis is very challenging.…”

Section: B Visual Food Analysismentioning

confidence: 99%

“…Existing multimedia research has made great progress in improving the recommendation performance and experience in many fields such as movies and POI, yet largely lags in the food domain. To the best of our knowledge, although relevant works on food recommendation [7], [8], [9] have received more attention in the multimedia community, there are very few systematic reviews, which provide a unified framework and comprehensive summary of current efforts in food recommendation. Because of huge potentials in food recommendation, the time has come for the multimedia field to give a survey on food recommendation, which can help researchers from relevant communities better understand the strength and weakness of existing methods.…”

Section: Introductionmentioning

confidence: 99%

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Food Recommendation: Framework, Existing Solutions, and Challenges

Min

Jiang

Jain

2020

IEEE Trans. Multimedia

101

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A growing proportion of the global population is becoming overweight or obese, leading to various diseases (e.g., diabetes, ischemic heart disease and even cancer) due to unhealthy eating patterns, such as increased intake of food with high energy and high fat. Food recommendation is of paramount importance to alleviate this problem. Unfortunately, modern multimedia research has enhanced the performance and experience of multimedia recommendation in many fields such as movies and POI, yet largely lags in the food domain. This article proposes a unified framework for food recommendation, and identifies main issues affecting food recommendation including building the personal model, analyzing unique food characteristics, incorporating various context and domain knowledge. We then review existing solutions for these issues, and finally elaborate research challenges and future directions in this field. To our knowledge, this is the first survey that targets the study of food recommendation in the multimedia field and offers a collection of research studies and technologies to benefit researchers in this field.

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Section: B Visual Food Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Food Recommendation: Framework, Existing Solutions, and Challenges

Min

Jiang

Jain

2020

IEEE Trans. Multimedia

101

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“…Furthermore, they set up a variable of time, so recommendations for cooking similar recipes would not happen every day. Moreover, Maruyama et al (Maruyama, 2012) recognized ingredients, through image processing techniques, in images that site users were taking on their mobile devices. The system could then recommend a suitable recipe that used the recognized ingredients.…”

Section: Related Workmentioning

confidence: 99%

Surprising Recipe Extraction based on Rarity and Generality of Ingredients

2014

Proceedings of the 6th International Conference on Agents and Artificial Intelligence

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Many surprising recipes that utilize different ingredients or cooking processes from normal recipes exist on user-generated recipe sites. The easiest way to find surprising recipes is to use the search function of the recipe sites. However, the titles of surprising recipes do not always include a keyword, such as "surprise", or an indication that a recipe is unusual in any way. Therefore, we cannot find surprising recipes very easily. In this paper, we propose a method to extract surprising or unique recipes from those user-generated recipe sites. We propose an RF-IIF (Recipe Frequency-Inverse Ingredient Frequency) based on TF-IDF (Term Frequency-Inverse Ingredient Frequency). First, we calculate the surprising value of the ingredients by using RF-IIF. Then, we calculate the surprising value of each recipe by summing the surprising values of the ingredients that appear in a recipe. Finally, we extract recipes that have high surprising values as surprising recipes of the dish category. In the evaluation experiment, the subjects requested an evaluation about each surprising recipe. As a result, we showed that the extracted recipes were valid recipes and also had a surprising or unusual element. Therefore, we showed the usefulness of the proposed method.

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“…See Figure 2(a) for multimedia presentation of the recipe for "Blueberry Crumb Cake". These information potentially provide opportunity for multi-modal analysis of recipes, including cuisine classification [27], food recognition [13,46], recipe recommendation [15,26] and cross-modal image-to-recipe search [2,5,28,35]. A common fundamental problem among these tasks is in the modeling of the cause-and-effect relations of this cooking workflow construction.…”

Section: Introductionmentioning

confidence: 99%

Multi-modal Cooking Workflow Construction for Food Recipes

Pan

Chen

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Understanding food recipe requires anticipating the implicit causal effects of cooking actions, such that the recipe can be converted into a graph describing the temporal workflow of the recipe. This is a non-trivial task that involves common-sense reasoning. However, existing efforts rely on hand-crafted features to extract the workflow graph from recipes due to the lack of large-scale labeled datasets. Moreover, they fail to utilize the cooking images, which constitute an important part of food recipes. In this paper, we build MM-ReS, the first large-scale dataset for cooking workflow construction, consisting of 9,850 recipes with human-labeled workflow graphs. Cooking steps are multi-modal, featuring both text instructions and cooking images. We then propose a neural encoder-decoder model that utilizes both visual and textual information to construct the cooking workflow, which achieved over 20% performance gain over existing hand-crafted baselines. CCS CONCEPTS • Information systems → Multimedia and multimodal retrieval.

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Real-time mobile recipe recommendation system using food ingredient recognition

Cited by 34 publications

References 7 publications

Food Recommendation: Framework, Existing Solutions, and Challenges

Food Recommendation: Framework, Existing Solutions, and Challenges

Surprising Recipe Extraction based on Rarity and Generality of Ingredients

Multi-modal Cooking Workflow Construction for Food Recipes

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