Authors :
Manne. Lahari; Maddu. Devi Prasanna; Eluri. Naveena Kumari; P.Srinu Vasarao
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/5n8jzc3y
Scribd :
https://tinyurl.com/3rt7ejrk
DOI :
https://doi.org/10.5281/zenodo.10797008
Abstract :
Amazon is the world's largest retailer by
revenue and business. Nearly a third of Amazon's sales
come from referrals, accounting for $470 billion of the
company's ecommerce revenue in 2021. The program is
called recommenders and uses machine learning to select
specific features from larger data sets. When there is a
lot of filtering, the suggested ideas are often based on
what the user has interacted with, purchased, viewed,
etc. Matches other similar products. The
recommendation engine recommends a product based on
this understanding of the user. This is how Amazon's
product recommendation engine works. The
recommendation engine filters products based on
product functionality and user characteristics (for
example, find other users who are similar to you and
have purchased the product you are looking at or will
buy). Amazon's recommendations use different filters to
recommend products. Amazon uses various artificial
intelligence algorithms to power all aspects of the
platform. To enable smart product search on the
Internet, the company also uses a proprietary technology
called A9. Amazon recently updated its A9 algorithm,
now called the A10 algorithm. The update changes many
aspects of the product's functionality, shifting the focus
of the product to the buyer's behaviour.
Machine learning algorithms in recommendations
generally fall into two groups: contextual methods and
collaborative filtering. Affiliate marketing is the most
common way to make online recommendations. It is
"collaborative" because it predicts a customer's
preferences based on other customers. A better way
would be to recommend the product based on the
relationship between the products and not on the
customer's consistency. Through user interaction,
Amazon.com visitors are matched with other customers
with similar purchasing history and personalized
recommendations are provided. Come and see. There
are many ways to create a unified consensus model.
Machine learning algorithms such as SVD and Top-k are
used to find the most popular products.
Keywords :
Recommendation System, Filtering Techniques, Artificial Intelligent Algorithms, A9 Algorithm, A10 Algorithm, Content-Based Filtering, Collaborative Filtering, SVD, Top-K.
Amazon is the world's largest retailer by
revenue and business. Nearly a third of Amazon's sales
come from referrals, accounting for $470 billion of the
company's ecommerce revenue in 2021. The program is
called recommenders and uses machine learning to select
specific features from larger data sets. When there is a
lot of filtering, the suggested ideas are often based on
what the user has interacted with, purchased, viewed,
etc. Matches other similar products. The
recommendation engine recommends a product based on
this understanding of the user. This is how Amazon's
product recommendation engine works. The
recommendation engine filters products based on
product functionality and user characteristics (for
example, find other users who are similar to you and
have purchased the product you are looking at or will
buy). Amazon's recommendations use different filters to
recommend products. Amazon uses various artificial
intelligence algorithms to power all aspects of the
platform. To enable smart product search on the
Internet, the company also uses a proprietary technology
called A9. Amazon recently updated its A9 algorithm,
now called the A10 algorithm. The update changes many
aspects of the product's functionality, shifting the focus
of the product to the buyer's behaviour.
Machine learning algorithms in recommendations
generally fall into two groups: contextual methods and
collaborative filtering. Affiliate marketing is the most
common way to make online recommendations. It is
"collaborative" because it predicts a customer's
preferences based on other customers. A better way
would be to recommend the product based on the
relationship between the products and not on the
customer's consistency. Through user interaction,
Amazon.com visitors are matched with other customers
with similar purchasing history and personalized
recommendations are provided. Come and see. There
are many ways to create a unified consensus model.
Machine learning algorithms such as SVD and Top-k are
used to find the most popular products.
Keywords :
Recommendation System, Filtering Techniques, Artificial Intelligent Algorithms, A9 Algorithm, A10 Algorithm, Content-Based Filtering, Collaborative Filtering, SVD, Top-K.
