Nearby Product Recommendation System Based on Users Rating

Jyoti, Akanksha; Roy, Abhijeet Deb; Singh, S.R.K.; Shaikh, Nabab; Desai, Payal

doi:10.32628/cseit1952268

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Among the nutrients needed in the human body, protein, fat, and carbohydrates are nutrients that can generate heat in the body during the process of human metabolism. It is an indispensable nutrient in the human body [21,22]. At the same time, these nutrients must be ingested in a certain proportion.…”

Section: Recommendation Accuracy Rate Of the Recommendationmentioning

confidence: 99%

Implementation of Key Technologies for a Healthy Food Culture Recommendation System Using Internet of Things

Xie

Lou

2022

Mobile Information Systems

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With the development of catering culture, the types of diets are changing with each passing day. The types of food are becoming more and more abundant, and the concept of healthy eating has become more and more prominent in people’s thinking. The development of Internet of Things technology enables people to live a variety of information in the network, and the amount of information increases sharply. With the development of the Internet of Things, the number of online recipes has increased significantly, and a number of choices are now available to people to find suitable recipes. It is still troublesome for people to select the menu of three meals on daily basis. In recent years, many people have long-term unhealthy eating habits, which has greatly increased the incidence of some diseases. Therefore, a balanced diet is very important for good health. This paper builds a fusion of healthy eating culture with the help of Internet of Things, deep learning, and other related technologies. A more practical healthy diet recommendation system is built, which aims to help people find diet recipes that conform to their personal eating preferences and habits and their health status. Experimental results show that the healthy food culture recommendation system constructed in this paper can more accurately recommend suitable healthy food recipes for individuals and promote people’s health. And the launched recipes can ensure nutrition and meet individual taste preferences, which has great practical significance.

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Section: Recommendation Accuracy Rate Of the Recommendationmentioning

confidence: 99%

Implementation of Key Technologies for a Healthy Food Culture Recommendation System Using Internet of Things

Xie

Lou

2022

Mobile Information Systems

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“…Integrate with a map interface to find the shortest distances among stores whose products were recommended. The result showed a better approach towards the recommendation of products among local stores within a region [11].…”

Section: Related Workmentioning

confidence: 98%

Home appliances recommendation system based on weather information using combined modified k-means and elbow algorithms

Jaafar

Gaata

Jasim

2020

IJEECS

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<p>The recommendation system is an intelligent system gives recommendations to users to discover the best interesting items. The purpose of this proposed recommendation system is to develop a system to find the best electrical devices according to weather conditions and user preferences. The proposed solution relies on the characteristics of electrical appliances and their suitability to weather conditions in any city. The proposed solution is the first recommendation system combines devices properties, weather conditions, and user preferences using a new combination of algorithms. The clustering algorithms are the most applicable in the field of recommendation system. The proposed solution relies on a combination of Elbow method, proposed modified K-means and Silhouette algorithm to find the best number of clusters before starting the clustering process. Then calculate the weights for each cluster and compare them with the weather weights to find the required clusters sorted from the near to far according to a computed threshold. The empirical results showed that the proposed solution demonstrated a 94% accuracy to match the characteristics of the recommended devices with the climatic characteristics of the region and user preferences. The accuracy is measured using Silhouette algorithm.</p>

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“…Jyoti et al [16] propose a collaborative filtering approach to recommend products based on active user' ratings. The proposed approach is the use of basic collaborative filtering without any improvement.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Ontology-Based Recommendation System in e-Commerce

2019

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The growth of the Internet has increased the amount of data and information available to any person at any time. Recommendation Systems help users find the items that meet their preferences, among the large number of items available. Techniques such as collaborative filtering and content-based recommenders have played an important role in the implementation of recommendation systems. In the last few years, other techniques, such as, ontology-based recommenders, have gained significance when reffering better active user recommendations; however, building an ontology-based recommender is an expensive process, which requires considerable skills in Knowledge Engineering. This paper presents a new hybrid approach that combines the simplicity of collaborative filtering with the efficiency of the ontology-based recommenders. The experimental evaluation demonstrates that the proposed approach presents higher quality recommendations when compared to collaborative filtering. The main improvement is verified on the results regarding the products, which, in spite of belonging to unknown categories to the users, still match their preferences and become recommended.

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Nearby Product Recommendation System Based on Users Rating

Cited by 3 publications

References 3 publications

Implementation of Key Technologies for a Healthy Food Culture Recommendation System Using Internet of Things

Implementation of Key Technologies for a Healthy Food Culture Recommendation System Using Internet of Things

Home appliances recommendation system based on weather information using combined modified k-means and elbow algorithms

A Hybrid Ontology-Based Recommendation System in e-Commerce

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