Recommendation System: A Contribution to Glycaemia Excursion Identification

2016 ◽  
pp. 1162-1166
Author(s):  
Lenka Lhotska ◽  
Miroslav Bursa ◽  
Michal Huptych ◽  
Katerina Stechova
Keyword(s):  
Recommendation System ◽  
System A

scholarly journals Product Recommendation System a Comprehensive Review

2021 ◽  
Vol 1022 ◽  
pp. 012021
Author(s):  
Jatin Sharma ◽  
Kartikay Sharma ◽  
Kaustubh Garg ◽  
Avinash Kumar Sharma
Keyword(s):  
Recommendation System ◽  
Comprehensive Review ◽  
Product Recommendation ◽  
System A

Personalized query recommendation system : A genetic algorithm approach

2020 ◽  
Vol 23 (2) ◽  
pp. 523-535 ◽  
Author(s):  
Debaditya Barman ◽  
Ritam Sarkar ◽  
Anil Tudu ◽  
Nirmalya Chowdhury
Keyword(s):  
Genetic Algorithm ◽  
Recommendation System ◽  
System A ◽  
Query Recommendation ◽  
Genetic Algorithm Approach

scholarly journals A Review on Outfit Fashion Recommendation System

2021 ◽  
pp. 220-222
Author(s):  
Bhagyshree Pravin Bhure ◽  
Pratiksha Tulshiram Bansod ◽  
Monali Shivram Amgaokar ◽  
Savita Pralhad Lodiwale ◽  
Anjali Pravin Orkey ◽  
...  
Keyword(s):  
Network Topology ◽  
Recommendation System ◽  
Web Applications ◽  
Living Standards ◽  
The Other ◽  
Convolutional Network ◽  
System A ◽  
Training Technique ◽  
The One ◽  
Time And Energy

With the quick rise in living standards, people's shopping passion grew, and their desire for clothing grew as well. A growing number of people are interested in fashion these days. However, when confronted with a large number of garments, consumers are forced to try them on multiple times, which takes time and energy. As a result of the suggested Fashion Recommendation System, a variety of online fashion businesses and web applications allow buyers to view collages of stylish items that look nice together. Clients and sellers benefit from such recommendations. On the one hand, customers can make smarter shopping decisions and discover new articles of clothes that complement one other. Complex outfit recommendations, on the other hand, assist vendors in selling more products, which has an impact on their business. FashionNet is made up of two parts: a feature network for extracting features and a matching network for calculating compatibility. A deep convolutional network is used to achieve the former. For the latter, a multi-layer completely connected network topology is used. For FashionNet, you must create and compare three different architectures. To achieve individualised recommendations, a two-stage training technique was created.

scholarly journals Product Recommendation System: A Systematic Literature Review

2021 ◽  
Vol 9 (VII) ◽  
pp. 3330-3339
Author(s):  
Ketki Kinkar
Keyword(s):  
Literature Review ◽  
Performance Measures ◽  
Systematic Literature Review ◽  
Recommender System ◽  
Recommendation System ◽  
Consumer Preferences ◽  
Research Work ◽  
Product Recommendation ◽  
System A ◽  
Scientific Papers

In today's world, we find a wide variety of search options and we may have difficulty selecting what we really need. The recommendation System plays an important part in dealing with these problems. A recommender system is a framework that is a filtering system that filters the data with various algorithms and recommends the user with the most relevant data. Recommendation systems are productive customization mechanisms, often up-to-date and recommendations based on current consumer preferences. These systems have shown to be extremely helpful in different areas of e-commerce, education, movies, music, books, films, scientific papers, and various products. This paper reviews many approaches of recommendation techniques with their upsides and downsides and diverse performance measures. We have reviewed various articles, analyzed their technique and approach, major features of the algorithm utilized, and potential areas for improvement in that research work.

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