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Author: 艾芙桑
Afsaneh Taheri
Thesis Title: 基於食材原料之食譜搜尋
Recipes Retrieval Based on Ingredients
Advisor: 楊傳凱
Chuan-Kai Yang
Committee: 林伯慎 
Bor-Shen Lin
賴源正 
Yuan-Cheng Lai
Degree: 碩士
Master
Department: 管理學院 - 資訊管理系
Department of Information Management
Thesis Publication Year: 2020
Graduation Academic Year: 108
Language: 英文
Pages: 50
Keywords (in Chinese):
Keywords (in other languages): Recipe Recommendation System, Tensorflow Object Detection, Recipe Retrieval, Ingredients Recognition, Faster RCNN
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  • Recommendation systems are offering users the ability to explore their interests and discover new things that are already popular in e-commerce websites and on online multimedia services. They will become interesting for food and recipe since they give users suggestions to select from the retrieved cooking based on food images or ingredients while possibly including personal preferences. This research proposes a recipe recommendation system that is based on the available ingredients with an additional feature to replace similar ingredients in the retrieved recipe. The system takes the images of the ingredients as input and recognizes them through a convolutional neural network (CNN). This research adopts Tensorflow Object Detection API to train the objects and a fine-tuned Faster R-CNN model with a training dataset for twelve ingredients. The detected ingredients can, in turn, be used to recommend recipes, and for each missing ingredient, our system finds the most suitable substitute ingredient that has large co-occurrence relations with the main ingredients with a higher frequency.

    Abstract V List of Tables VIII List of Figure IX Chapter 1: Introduction 1 Chapter 2: Literature Review 4 Chapter 3: Methodology 6 3.1 Object Detection using Tensorflow 6 3.2 Faster RCNN 6 3.3 Data Collection 8 3.4 Database 10 Relation among ingredients.............................11 3.5 Problem Formulation...................................12 3.6 Proposed Architecture................................. 13 3.7 Ingredients list.................................................15 Chapter 4: Results and Discussion..............16 4.2 Django Framework.........................................16 4.2 Training Results...............................................18 4.4 frequency of ingredient...............................20 4.4 User Study.........................................................21 Chapter 5: Conclusion & Future work............23 References .............................................................24 Appendix 1 Recipes. ...........................................27 Appendix 2 Ingredients. ...................................29 Appendix 3 User Study Questionnaire...........32

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