A Chinese dishes recommendation algorithm based on personal taste

He, Ningxuan; Liu, Mengyuan; Zhao, Fang

doi:10.1109/cybconf.2015.7175946

Cited by 4 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the disadvantage is that it requires users to score food explicitly. The authors in Reference 14 proposed a Chinese dishes recommendation algorithm based on personal taste. The algorithm quantified one's taste based on acid, sweet, bitter, spicy, and salty as indicators by using k‐means clustering method and determined the number of user's favorite tastes by BWP index.…”

Section: Related Workmentioning

confidence: 99%

Privacy‐preserving dish‐recommendation for food nutrition through edging computing

Qiao

Sun

Cao

et al. 2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

The catering industry is a humungous service-based industry, which includes food nutrition and security, transporting, tourism, and similar services. Employing the new techniques, such as machine learning and deep learning, competing firms can improve their strategies and deliver better services with lower price.In this work, we investigate a large number of historical dining data that reflect customer spending habits and taste preferences through terminal devices at the edge of the network, which can be used for customers to recommend dishes when ordering. With the popularity of cloud computing today, it is also prone to some shortcomings when the amount of computing is too large, such as poor real-time performance, high server pressure, data security risks, and so on. Especially, the leakage of user privacy often has incalculable consequences. Based on this, this article proposes a hot pot dish recommendation algorithm based on data mining and food nutrition for edge devices, which can effectively protect user privacy. The experimental data in this article is derived from the massive consumption data of customers in China's large hot pot enterprises. First, clean the raw data and mask the user privacy to get feature data unrelated with user privacy that can be used for dish recommendation. On the basis of this, combined with data mining methods, diet, and other indicators, the results of final recommended dish can respond to both user preferences and nutrition arrange.The experimental results show that this dish-recommended method is more reasonable and healthier.

show abstract

Section: Related Workmentioning

confidence: 99%

Privacy‐preserving dish‐recommendation for food nutrition through edging computing

Qiao

Sun

Cao

et al. 2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…Recommendations were produced by weighting each ingredient. He et al ( 46 ) used tastes (sour, sweet, bitter, spicy, and salty) to compose a vector of flavors for each dish. Then, customers' food ordering records and the established flavor vectors were used to make recommendations.…”

Section: Introductionmentioning

confidence: 99%

Application of Intelligent Recommendation Techniques for Consumers' Food Choices in Restaurants

Jia

Yang

et al. 2018

Front. Psychiatry

View full text Add to dashboard Cite

Currently, there has been a new trend in applying modern robotics, information technology, and artificial intelligence to restaurants for improvements of food service, cost-effectiveness, and customer satisfaction. As robots replace humans to serve food, there is a clear need for robotic servers to help consumers select foods from a menu that satisfies their preferences such as taste and nutrition. However, currently, little is known about how eating behaviors drive food choices, and it is often difficult for consumers to make choices from a variety of foods offered by the typical restaurant, even with the assistance from a human server. In this paper, we conduct an exploratory study on an intelligent food choice method that recommends dishes by predicting individual's dietary preference, including ingredients, types of spices, price, etc. A multi-attribute relation matrix tri-factorization (MARMTF) technique is developed for a relation-driven food recommendation system. First, the user's ordering history and their rating scores of the foods in the menu are gathered and represented by a user-dish rating matrix. Next, the attribute relations of the ingredients, spicy level, and price of each food choice are extracted to construct a group of the relation matrices. Then, these matrices are integrated into a large block matrix. In the next step, a matrix tri-factorization algorithm is employed to decompose the block matrix and fuse the complex relationships into matrix factors. Further, a set of approximation block matrices are constructed and the predicted food rating matrix is generated. Finally, the foods (dishes) with sufficiently high preference scores are recommended to the consumers. Our experiments demonstrate that the MARMTF technique can provide effective dish recommendation for customers. Our system significantly simplifies the daunting task of making food choices and has a great potential in providing intelligent and professionally trained non-human waiters and waitresses for employment by future restaurants.

show abstract

An elastic group recommendation system designed for multivariate dynamic attributes

Mysore¹

2016

2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS)

View full text Add to dashboard Cite

A Chinese dishes recommendation algorithm based on personal taste

Cited by 4 publications

References 10 publications

Privacy‐preserving dish‐recommendation for food nutrition through edging computing

Privacy‐preserving dish‐recommendation for food nutrition through edging computing

Application of Intelligent Recommendation Techniques for Consumers' Food Choices in Restaurants

An elastic group recommendation system designed for multivariate dynamic attributes

Contact Info

Product

Resources

About