Machine Learning Based Food Recipe Recommendation System

Vivek, M. B.; Manju, N.; Vijay, M. B.

doi:10.1007/978-981-10-5146-3_2

Cited by 30 publications

(10 citation statements)

References 9 publications

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“…A few of the approaches proposed include user information into the recommendation procedures (i.e., collaborative filtering). Still, they only considered similar users based on the overlapping rated recipes, ignoring the relational information among users, recipes, or ingredients (Freyne and Berkovsky, 2010 ; Forbes and Zhu, 2011 ; Ge et al, 2015 ; Vivek et al, 2018 ; Khan et al, 2019 ; Gao et al, 2020 ). For example, Yang et al ( 2017 ) developed a framework to learn food preference based on the item-wise and pairwise recipe image comparisons.…”

Section: Related Workmentioning

confidence: 99%

“…Existing recipe recommendation approaches are mostly based on the similarity between recipes (Yang et al, 2017 ; Chen et al, 2020 ). A few of the approaches tried to take the user information into account (Freyne and Berkovsky, 2010 ; Forbes and Zhu, 2011 ; Ge et al, 2015 ; Vivek et al, 2018 ; Khan et al, 2019 ; Gao et al, 2020 ), but they only defined similar users based on the overlapping rated recipes between users, while ignoring the relational information between users, recipes, or ingredients. Nevertheless, user preference toward food is complex.…”

Section: Introductionmentioning

confidence: 99%

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Recipe Recommendation With Hierarchical Graph Attention Network

et al. 2022

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Recipe recommendation systems play an important role in helping people find recipes that are of their interest and fit their eating habits. Unlike what has been developed for recommending recipes using content-based or collaborative filtering approaches, the relational information among users, recipes, and food items is less explored. In this paper, we leverage the relational information into recipe recommendation and propose a graph learning approach to solve it. In particular, we propose HGAT, a novel hierarchical graph attention network for recipe recommendation. The proposed model can capture user history behavior, recipe content, and relational information through several neural network modules, including type-specific transformation, node-level attention, and relation-level attention. We further introduce a ranking-based objective function to optimize the model. Thorough experiments demonstrate that HGAT outperforms numerous baseline methods.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recipe Recommendation With Hierarchical Graph Attention Network

et al. 2022

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“…Procedure: /*This program helps the user in the finding the recipe in which the user is interested*/ Begin [1] The input of the form like speech, image and text is taken from the user onto the website. [2] If the input is of the form image then…”

Section: P(y | X1…xn) = [ P(y)∏i=1 To N P(xi | Y) ] / P(x1…xn)mentioning

confidence: 99%

A Culling System for a Recipe using Sentiment Analysis Techniques

Mohan¹,

Meher²,

Azhagiri³

et al. 2019

IJEAT

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This System helps the user for providing the recipes in which they are particularly interested in a mentioned criteria of ingredients on which the user wants to prepare a recipe. The input given by the user like the ingredient on which he/she wants to prepare a recipe can be of text, speech or visual. The process of providing a list of gathered recipes from different trusted and verified sources is performed with the help of Sentiment Analysis(SA), Watson Text-to-Speech and Watson Visual Recognition(WVR). The recipe extraction from different sources which are required for the user is retrieved with the help of a standard Web Crawler. The tools and technologies used for the proposed system are from Artificial Intelligence(AI), Natural Language Processing(NLP). The system proposed assists the user in providing a list of recipes in a prioritized order based on the optimization process performed by the Naïve Bayes Algorithm(NBA) of Sentiment Analysis. In addition, the displayed results of recipes have been reviewed and rated by different users from different sources.

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“…Besides above approach, in this paper [24] authors presented the item-based approach and the user based approach to recommend recipes based on preferences of the user ratings. Tanimoto Coefficient Similarity and Log Likelihood Similarity were used to compute similarities between different recipes for the item-based approach.…”

Section: Improving Recommender Systems With Machine Learningmentioning

confidence: 99%

Ontology-based Recommender System in Higher Education

Obeid

Lahoud

Khoury

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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Academic advising is limited in its ability to assist students in identifying academic pathways. Selecting a major and a university is a challenging process rife with anxiety. Students at high school are not sure how to match their interests with their working future or major. Therefore, high school students need guidance and support. Moreover, students need to filter, prioritize and efficiently get appropriate information from the web in order to solve the problem of information overload. This paper represents an approach for developing ontology-based recommender system improved with machine learning techniques to orient students in higher education. The proposed recommender system is an assessment tool for students' vocational strengths and weaknesses, interests and capabilities. The main objective of our ontology-based recommender system is to identify the student requirements, interests, preferences and capabilities to recommend the appropriate major and university for each one.

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Machine Learning Based Food Recipe Recommendation System

Cited by 30 publications

References 9 publications

Recipe Recommendation With Hierarchical Graph Attention Network

Recipe Recommendation With Hierarchical Graph Attention Network

A Culling System for a Recipe using Sentiment Analysis Techniques

Ontology-based Recommender System in Higher Education

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