ENG
ENG
ENG
With AI-powered suggestions, you can boost customer loyalty and spending. This code can be used on your home page, product information pages, email promotions, and much more.
Recommender Systems
Recommender Engine
That Drives You Forward
Contact Us
What Is Recommender Systems?
The fundamental goal of a recommender system is to reduce information overload and to provide personalized suggestions that can assist the users in the decision-making process.
Music
What music to listen to
(Spotify)
Movie
What movies to watch
(Netflix)
Product
What consumer products to
purchase (Amazon, AliExpress)
Suggest groups to join,
people to follow, videos to
watch, or posts to like
Recommender systems are information search and filtering tools that help users to discover relevant items and to make better choices while searching for products or services such as movies, books, vacations, or electronic products.  In a general way, recommender systems are algorithms aimed at suggesting relevant items to users. (Items being movies to watch, text to read, products to buy or anything else depending on industries.)
Types of
Recommender Systems
Similarity Based Filtering
01
The user will be recommended items similar to those he/she enjoyed in the past.
The main characteristics of user-user and item-item approaches it that they use
only information from the user-item interaction matrix and they assume no
model to produce new recommendations
Content Based Filtering
02
Content based approaches use additional information about users and/or items
It is needed to collect and set some specific features to represent items and users.
Then the machine learning or deep learning model will be trained by using
the combined features.
Location Based Filtered
03
These services take advantage of the increasing use of smartphones that store
and provide the location information of their users
This could include recommendations for restaurants, museums, or other points of interest
or events near the user’s location.
Matrix Factorization
05
It is one of the techniques used in collaborative model-based filtering
According to a certain number of hidden features the user/item matrix is splitted into users and
items matrices and the rating score is predicted. Then the items with the highest rates will be
recommended to each user.
Collaborative Filtering
04
Recommendations are made based on items consumed by users whose tastes
and preferences are similar to that of the referred user.
Memory-based algorithms act on the entire user/item matrix. They assume that users/items
can be grouped together by similarity.model-based techniques use a set of
user assessments to generate an estimated model, saving the parameters learned
during training. Instead of using similarity measurements, these algorithms
are characterized by the creation of models.
Learn how Tractus can help your business
Contact Us
Learn how Tractus can help your business
Contact Us
Matrix Factorization
05
It is one of the techniques used in collaborative model-based filtering
According to a certain number of hidden features the user/item matrix is splitted into users and items matrices and the rating score is predicted. Then the items with the highest rates will be
recommended to each user.
Collaborative Filtering
04
Recommendations are made based on items consumed by users whose tastes
and preferences are similar to that of the referred user.
Memory-based algorithms act on the entire user/item matrix. They assume that users/items can be grouped together by similarity.model-based techniques use a set of
user assessments to generate an estimated model, saving the parameters learned during training. Instead of using similarity measurements, these algorithms are characterized by the creation of models.
Similar Users
User 1
User 2
Read by user 1,
recommended to user 2
Read by both users
Location Based Filtered
03
These services take advantage of the increasing use of smartphones that store and provide the location information of their users
This could include recommendations for restaurants, museums, or other points of interest or events near the user’s location.
Content Based Filtering
02
Content based approaches use additional information about users and/or items
It is needed to collect and set some specific features to represent items and users. Then the machine learning or deep learning model will be trained by using  the combined features.
Similarity Based Filtering
01
The user will be recommended items similar to those he/she enjoyed in the past.
The main characteristics of user-user and item-item approaches it that they use only information from the user-item interaction matrix and they assume no model to produce new recommendations
Types of
Recommender Systems
What Is Recommender Systems?
The fundamental goal of a recommender system is to reduce information overload and to provide personalized suggestions that can assist the users in the decision-making process.
Suggest groups to join,
people to follow, videos to
watch, or posts to like
What consumer products to
purchase (Amazon, AliExpress)
Product
What movies to watch
(Netflix)
Movie
What music to listen to
(Spotify)
Music
Recommender systems are information search and filtering tools that help users to discover relevant items and to make better choices while searching for products or services such as movies, books, vacations, or electronic products.  In a general way, recommender systems are algorithms aimed at suggesting relevant items to users. (Items being movies to watch, text to read, products to buy or anything else depending on industries.)
With AI-powered suggestions, you can boost customer loyalty and spending. This code can be used on your home page, product information pages, email promotions, and much more.
Recommender Systems
Recommender Engine
That Drives You Forward
Contact Us
Learn how Tractus can help
your business
Contact Us
Matrix Factorization
05
It is one of the techniques used in collaborative model-based filtering
According to a certain number of hidden features the user/item matrix is splitted into users and items matrices and the rating score is predicted. Then the items with the highest rates will be
recommended to each user.
Collaborative Filtering
04
Recommendations are made based on items consumed by users whose tastes and preferences are similar to that of the referred user.
Memory-based algorithms act on the entire user/item matrix. They assume that users/items can be grouped together by similarity.model-based techniques use a set of user assessments to generate an estimated model, saving the parameters learned during training. Instead of using similarity measurements, these algorithms are characterized by the creation of models.
Similar Users
User 1
User 2
Read by user 1,
recommended to user 2
Read by both users
Location Based Filtered
03
These services take advantage of the increasing use of smartphones that store and provide the location information of their users
This could include recommendations for restaurants, museums, or other points of interest or events near the user’s location.
Content Based Filtering
02
Content based approaches use additional information about users and/or items
It is needed to collect and set some specific features to represent items and users. Then the machine learning or deep learning model will be trained by using  the combined features.
Similarity Based Filtering
01
The user will be recommended items similar to those he/she enjoyed in the past.
The main characteristics of user-user and item-item approaches it that they use only information from the user-item interaction matrix and they assume no model to produce new recommendations
Types of
Recommender Systems
What Is Recommender Systems?
The fundamental goal of a recommender system is to reduce information overload and to provide personalized suggestions that can assist the users in the decision-making process.
Suggest groups to join,
people to follow, videos to watch, or posts to like
What consumer products
to purchase
(Amazon, AliExpress)
Product
What movies to watch
(Netflix)
Movie
What music to listen to
(Spotify)
Music
Recommender systems are information search and filtering tools that help users to discover relevant items and to make better choices while searching for products or services such as movies, books, vacations, or electronic products.  In a general way, recommender systems are algorithms aimed at suggesting relevant items to users. (Items being movies to watch, text to read, products to buy or anything else depending on industries.)
With AI-powered suggestions, you can boost customer loyalty and spending. This code can be used on your home page, product information pages, email promotions, and much more.
Recommender Systems
Recommender Engine
That Drives You Forward
Contact Us
Learn how Tractus can
help your business
Contact Us
Matrix Factorization
05
It is one of the techniques used in collaborative model-based filtering
According to a certain number of hidden features the user/item matrix is splitted into users and items matrices and the rating score is predicted. Then the items with the highest rates will be recommended to each user.
Collaborative Filtering
04
Recommendations are made based on items consumed by users whose tastes and preferences are similar to that of the referred user.
Memory-based algorithms act on the entire user/item matrix. They assume that users/items can be grouped together by similarity.model-based techniques use a set of user assessments to generate an estimated model, saving the parameters learned during training. Instead of using similarity measurements, these algorithms are characterized by the creation of models.
Similar Users
User 1
User 2
Read by user 1,
recommended to user 2
Read by both users
Location Based Filtered
03
These services take advantage of the increasing use of smartphones that store and provide the location information of their users
This could include recommendations for restaurants, museums, or other points of interest or events near the user’s location.
Content Based Filtering
02
Content based approaches use additional information about users and/or items
It is needed to collect and set some specific features to represent items and users. Then the machine learning or deep learning model will be trained by using  the combined features.
Similarity Based Filtering
01
The user will be recommended items similar to those he/she enjoyed in the past.
The main characteristics of user-user and item-item approaches it that they use only information from the user-item interaction matrix and they assume no model to produce new recommendations
Types of
Recommender Systems
What Is Recommender Systems?
The fundamental goal of a recommender system is to reduce information overload and to provide personalized suggestions that can assist the users in the decision-making process.
Suggest groups to join, people to follow, videos to watch, or posts to like
What consumer
products to
purchase
Product
What movies
to watch
(Netflix)
Movie
What music to
listen to
(Spotify)
Music
Recommender systems are information search and filtering tools that help users to discover relevant items and to make better choices while searching for products or services such as movies, books, vacations, or electronic products.  In a general way, recommender systems are algorithms aimed at suggesting relevant items to users. (Items being movies to watch, text to read, products to buy or anything else depending on industries.)
With AI-powered suggestions, you can boost customer loyalty and spending. This code can be used on your home page, product information pages, email promotions, and much more.
Recommender
Systems
Recommender Engine
That Drives You Forward
Contact Us
Learn how Tractus can
help your business
Contact Us
Matrix Factorization
05
It is one of the techniques used in collaborative model-based filtering
According to a certain number of hidden features the user/item matrix is splitted into users and items matrices and the rating score is predicted. Then the items with the highest rates will be recommended to each user.
Collaborative Filtering
04
Recommendations are made based on items consumed by users whose tastes and preferences are similar to that of the referred user.
Memory-based algorithms act on the entire user/item matrix. They assume that users/items can be grouped together by similarity.model-based techniques use a set of user assessments to generate an estimated model, saving the parameters learned during training. Instead of using similarity measurements, these algorithms are characterized by the creation of models.
Similar Users
User 1
User 2
Read by user 1,
recommended to user 2
Read by both users
Location Based Filtered
03
These services take advantage of the increasing use of smartphones that store and provide the location information of their users
This could include recommendations for restaurants, museums, or other points of interest or events near the user’s location.
Content Based Filtering
02
Content based approaches use additional information about users and/or items
It is needed to collect and set some specific features to represent items and users. Then the machine learning or deep learning model will be trained by using  the combined features.
Similarity Based Filtering
01
The user will be recommended items similar to those he/she enjoyed in the past.
The main characteristics of user-user and item-item approaches it that they use only information from the user-item interaction matrix and they assume no model to produce new recommendations
Types of
Recommender Systems
What Is Recommender Systems?
The fundamental goal of a recommender system is to reduce information overload and to provide personalized suggestions that can assist the users in the decision-making process.
Suggest groups to join, people to follow, videos to watch, or posts to like
What consumer
products to
purchase
Product
What movies
to watch
(Netflix)
Movie
What music to
listen to
(Spotify)
Music
Recommender systems are information search and filtering tools that help users to discover relevant items and to make better choices while searching for products or services such as movies, books, vacations, or electronic products.  In a general way, recommender systems are algorithms aimed at suggesting relevant items to users. (Items being movies to watch, text to read, products to buy or anything else depending on industries.)
With AI-powered suggestions, you can boost customer loyalty and spending. This code can be used on your home page, product information pages, email promotions, and much more.
Recommender
Systems
Recommender Engine
That Drives You Forward
Contact Us
Learn how Tractus can help your business
Contact Us
Matrix Factorization
05
It is one of the techniques used in collaborative model-based filtering
According to a certain number of hidden features the user/item matrix is splitted into users and items matrices and the rating score is predicted. Then the items with the highest rates will be recommended to each user.
Collaborative Filtering
04
Recommendations are made based on items consumed by users whose tastes and preferences are similar to that of the referred user.
Memory-based algorithms act on the entire user/item matrix. They assume that users/items can be grouped together by similarity.model-based techniques use a set of user assessments to generate an estimated model, saving the parameters learned during training. Instead of using similarity measurements, these algorithms are characterized by the creation of models.
Similar Users
User 1
User 2
Read by user 1,
recommended to user 2
Read by both users
Location Based
Filtered
03
These services take advantage of the increasing use of smartphones that store and provide the location information of their users
This could include recommendations for restaurants, museums, or other points of interest or events near the user’s location.
Content Based
Filtering
02
Content based approaches use additional information about users and/or items
It is needed to collect and set some specific features to represent items and users. Then the machine learning or deep learning model will be trained by using  the combined features.
Similarity Based
Filtering
01
The user will be recommended items similar to those he/she enjoyed in the past.
The main characteristics of user-user and item-item approaches it that they use only information from the user-item interaction matrix and they assume no model to produce new recommendations
Types of
Recommender Systems
What Is Recommender Systems?
The fundamental goal of a
recommender system is to
reduce information overload
and to provide personalized
suggestions that can assist
the users in the decision-
making process.
Suggest groups to join, people to follow, videos to watch, or posts to like
What consumer
products to
purchase
Product
What movies
to watch
(Netflix)
Movie
What music to
listen to
(Spotify)
Music
Recommender systems are information search and filtering tools that help users to discover relevant items and to make better choices while searching for products or services such as movies, books, vacations, or electronic products.  In a general way, recommender systems are algorithms aimed at suggesting relevant items to users. (Items being movies to watch, text to read, products to buy or anything else depending on industries.)
With AI-powered suggestions, you can boost customer loyalty and spending. This code can be used on your home page, product information pages, email promotions, and much more.
Recommender
Systems
Recommender Engine
That Drives You Forward
Contact Us