A recommendation system, often referred to as a recommender system or recommendation engine, is a type of machine learning application that provides personalized suggestions or recommendations to users. These systems are widely used in various domains to help users discover products, services, or content that are likely to be of interest to them. There are several approaches to building recommendation systems in machine learning:

1. Machine Learning
Unit V
Recommended System
By: Dr. Shweta Saraswat

3. What Are Recommendation Systems in
Machine Learning?
• Recommended systems are the systems that are designed to recommend things to the user based
on many different factors. These systems predict the most likely product that the users are most
likely to purchase and are of interest to.
• Companies like Netflix, Amazon, etc. use recommender systems to help their users to identify the
correct product or movies for them.
• The recommender system deals with a large volume of information present by filtering the most
important information based on the data provided by a user and other factors that take care of the
user’s preference and interest. It finds out the match between user and item and imputes the
similarities between users and items for recommendation.
• Both the users and the services provided have benefited from these kinds of systems. The quality
and decision-making process has also improved through these kinds of systems.
Recommen
dation

4. Why the Recommendation system?
•Benefits users in finding items of their interest.
•Help item providers in delivering their items to the right user.
•Identity products that are most relevant to users.
•Personalized content.
•Help websites to improve user engagement.
What can be Recommended?
There are many different things that can be recommended by the system like
movies, books, news, articles, jobs, advertisements, etc. Netflix uses a
recommender system to recommend movies & web-series to its users. Similarly,
YouTube recommends different videos. There are many examples of recommender
systems that are widely used today.

5. How do User and Item matching is done?
In order to understand how the item is recommended and how the matching is
done, let us a look at the images below;

6. Perfect matching may not be recommended
The above pictures show that there won't be any perfect recommendation which is made to
a user. In the above image, a user has searched for a laptop with 1TB HDD, 8GB ram, and
an i5 processor for 40,000₹. The system has recommended 3 most similar laptops to the
user.

7. Types of Recommend System
1. Popularity-Based Recommendation System
It is a type of recommendation system which works on the principle of popularity and or
anything which is in trend. These systems check about the product or movie which are in trend
or are most popular among the users and directly recommend those.
For example, if a product is often purchased by most people then the system will get to know
that that product is most popular so for every new user who just signed it, the system will
recommend that product to that user also and chances becomes high that the new user will also
purchase that.
Merits of popularity based recommendation system
It does not suffer from cold start problems which means on day 1 of the business also it can
recommend products on various different filters.
•There is no need for the user's historical data.

8. Demerits of popularity based recommendation system
Not personalized
•The system would recommend the same sort of products/movies which are solely
based upon popularity to every other user.
Example
•Google News: News filtered by trending and most popular news.
•YouTube: Trending videos.

9. 2. Classification Model
 The model that uses features of both products as well as users to predict whether a user will
like a product or not.
The output can be either 0 or 1. If the user likes it then 1 and vice-versa.
Limitations of Classification Model
It is a rigorous task to collect a high volume of information about different users and also products.
•Also, if the collection is done then also it can be difficult to classify.
•Flexibility issue.

10. 3. Content-Based Recommendation System
It is another type of recommendation system which works on the principle of
similar content. If a user is watching a movie, then the system will check about
other movies of similar content or the same genre of the movie the user is
watching. There are various fundamentals attributes that are used to compute
the similarity while checking about similar content.
To explain more about how exactly the system works, an example is stated
below:

11. But How Is It Recommended?
To check the similarity between the products or mobile phone in this
example, the system computes distances between them. One plus 7 and One
plus 7T both have 8Gb ram and 48MP primary camera.
If the similarity is to be checked between both the products, Euclidean
distance is calculated. Here, distance is calculated based on ram and camera;

12. • Euclidean distance between (7T,7) is 0 whereas Euclidean distance between (7pro,7) is 4
which means one plus 7 and one plus 7T have similarities in them whereas one plus 7Pro
and 7 are not similar products.
• In order to explain the concept through this example, only the basic thing (camera and
ram) was taken but there is no restriction. We can compute distance calculation for any of
the features of the product. The basic principle remains the same if the distance between
both is 0, they are likely to have similar content.
• There are different scenarios where we need to check about the similarities, so there are
different metrics to be used. For computing the similarity between numeric data,
Euclidean distance is used, for textual data, cosine similarity is calculated and for
categorical data, Jaccard similarity is computed.

13. Merits
•There is no requirement for much of the user’s data.
•We just need item data that enable us to start giving recommendations to users.
•A content-based recommender engine does not depend on the user’s data, so
even if a new user comes in, we can recommend the user as long as we have
the user data to build his profile.
•It does not suffer from a cold start.
Demerits
•Items data should be in good volume.
•Features should be available to compute the similarity.

14. 4. Collaborative Filtering
• It is considered to be one of the very smart recommender systems that work
on the similarity between different users and also items that are widely used
as an e-commerce website and also online movie websites. It checks about
the taste of similar users and does recommendations.
• The similarity is not restricted to the taste of the user moreover there can
be consideration of similarity between different items also. The system will
give more efficient recommendations if we have a large volume of
information about users and items.

15. Concept of collaborative filtering.

16.  This is the way collaborative filtering works. Mainly, there are two approaches
used in collaborative filtering stated below;
a) User-based nearest-neighbor collaborative filtering
Figure shows user-user collaborative filtering where there are three users A, B
and C respectively and their interest in fruit. The system finds out the users who
have the same sort of taste of purchasing products and similarity between users is
computed based upon the purchase behavior. User A and User C are similar
because they have purchased similar products.

17. b)Item-based nearest-neighbour collaborative filtering
Figure shows user X, Y, and Z respectively. The system checks the items that
are similar to the items the user bought. The similarity between different items
is computed based on the items and not the users for the prediction. Users X
and Y both purchased items A and B so they are found to have similar tastes.

18. Limitations
• Enough users required to find a match. To overcome such cold start problems, often
hybrid approaches are made use of between CF and Content-based matching.
• Even if there are many users and many items that are to be recommended often,
problems can arise of user and rating matrix to be sparse and will become challenging to
find out about the users who have rated the same item.
• The problem in recommending items to the user due to sparsity problems.

19. Introduction to Neural Network
• Neural Networks are networks of neurons, for example, as found in real (i.e.biological) brains.
• Artificial neurons are crude approximations of the neurons found in real
brains.Theymay be physical devices,orpurelymathematical constructs.
• Artificial Neural Networks (ANNs) are networks of Artificial Neurons and
hence constitutecrude approximationsto partsof realbrains.

20. Basic Structure of ANNs
• The idea of ANNs is based on the belief that working of human brain by making the right
connections, can be imitated using silicon and wiresas living neurons and dendrites.
• The human brain is composed of 86 billion nerve cells called neurons. They are connected to
otherthousand cells by Axons.
• A neuron can then send the message to other neuron to handle the issue or does not send it
forward.
• ANNs are composed of multiple nodes, which imitate biological neurons of
human brain.
• The neurons are connected by links and they interact with each other. The nodes can take input
data and perform simple operations on the data. The result of these operations is passed to other
neurons.Theoutput at each node iscalled itsactivation ornode value.
• Each link is associated with weight. ANNs are capable of learning, which takes place by
alteringweight values.

21. Basic Structure of ANN
“Artificial Neural Networks or ANN is an information processing paradigm that is inspired by
the way the biological nervous system such as brain process information. It is composed of large
number of highly interconnected processing elements(neurons) working in unison to solve a
specific problem.”

22. Working of ANNs
• Each arrow represents a connection between two neurons
and indicates the pathway for the flow of information. Each
connection has a weight, an integer number that controls
the signal between the two neurons.
• If the network generates a “good or desired” output, there is
no need to adjust the weights. However, if the network
generates a “poor or undesired” output or an error, then the
system alters the weights in order to improve subsequent
results.

23. Perceptron
• A perceptron
network unit
neuron) that
is a neural
(an artificial
does certain
to detect
computations
features or business
intelligence in the input data.
• Inputs are summed and
passed through a nonlinear
function to produce output.
• Every neuron holds an internal
state called activation signal.
• Every neuron is connected to
another neuron via
connection link

24. Perceptron Function
Perceptron is a function that maps its input “x,” which is multiplied with the
learned weight coefficient; an output value ”f(x)”is generated. In the equation
given above:
w = vector of real-valued weights
b = bias (an element that adjusts the boundary away from origin without any
dependence on the input value)
x = vector of input x values

25. Perceptron Learning Rule
• Perceptron Learning Rule states that the algorithm would
automatically learn the optimal weight coefficients. The input features
are then multiplied with these weights to determine if a neuron fires or
not.
• The Perceptron receives multiple input signals, and if the sum of the
input signals exceeds a certain threshold, it either outputs a signal or
does not return an output.

26. Types of Artificial Neural Networks
• There are two Artificial Neural Network topologies −Feed-Forward and Feedback.
Feed-Forward ANN
• In this ANN, the information flow is unidirectional. A unit sends information to other
unit from which it does not receive any information. There are no feedback loops.
• They are used in pattern generation/recognition /classification. They have fixed
inputs and outputs.
• Two different classes :
• Single Layer Feed Forward
• Multi Layer Feed Forward
Feedback ANN
• Feedback loops are allowed. They are used in content addressable memories.

27. Single Layer Feed-Forward
• The simplest kind of neural
network is a single layer
perceptron network, which
consists of a single layer of
output nodes; the inputs are
fed directly to the output
nodes via a series of weights.
• Sum of the products of the
weights and the inputs is
calculated in each node. If
the value is above some
threshold the neuron
the activated value
otherwise it
takes
and
takes
deactivated values.

28. Multi Layer Feed-Forward
• This class of networks
consists of multiple layers of
computational units,
usually interconnected in a
feed forward way.
• Each neuron in one layer
has directed connections
to the neurons of the next
layer.
• The units of these networks
apply a sigmoid function
as an activation function.

29. Machine Learning in ANNs
• ANNs are capable of learning and they need to be trained. There are
several learning strategies−
• Supervised Learning −It involves a teacher that is scholar than the ANN
itself. For example, the teacher feeds some example data about which the
teacher already knows the answers. Forexample, pattern recognizing.
• Unsupervised Learning −It is required when there is no example data set
with known answers. For example, searching for a hidden pattern. In this
case, clustering i.e. dividing a set of elements into groups according to
some unknown pattern is carried out based on the existingdata setspresent.
• Reinforcement Learning − This strategy built on observation. The ANN
makes a decision by observing its environment. If the observation is
negative, the network adjusts its weights to be able to make a different
required decision the nexttime.

30. Inroduction to Backpropagation
• Backpropagation is a multi layered feed forward, supervised learning
network based on gradient descent learning rule( a first order
repeated optimization algorithm).
• It calculates gradient of a loss function with respect to all the weights
in the network, so that the gradient is fed to the optimization method
which in turn uses it to update the weights, in an attempt to minimize
the loss function.
• Propagates backward to adjust for errors from outputs to hidden layers
to inputs

31. Backpropagation Algorithm
• Computes error term for the output units using the observed error
.
• From output layer, repeat
o Propagating the error term back to the previous layer and
o Updating the weights between the two layers until the earliest
hidden layer isreached.

32. Introduction to Deep Learning
Deep learning is a branch of machine learning which is based on artificial
neural networks. It is capable of learning complex patterns and relationships
within data. In deep learning, we don’t need to explicitly program
everything. It has become increasingly popular in recent years due to the
advances in processing power and the availability of large datasets. Because
it is based on artificial neural networks (ANNs) also known as deep neural
networks (DNNs). These neural networks are inspired by the structure and
function of the human brain’s biological neurons, and they are designed to
learn from large amounts of data.

33. Introduction to Deep Learning

34. What is Deep Learning?
Deep learning is the branch of machine learning which is based on artificial neural
network architecture. An artificial neural network or ANN uses layers of interconnected
nodes called neurons that work together to process and learn from the input data.
In a fully connected Deep neural network, there is an input layer and one or more
hidden layers connected one after the other. Each neuron receives input from the
previous layer neurons or the input layer. The output of one neuron becomes the input
to other neurons in the next layer of the network, and this process continues until the
final layer produces the output of the network. The layers of the neural network
transform the input data through a series of nonlinear transformations, allowing the
network to learn complex representations of the input data.

36. applications
Today Deep learning has become one of the most popular and
visible areas of machine learning, due to its success in a variety of
applications, such as computer vision, natural language processing,
and Reinforcement learning.
Deep learning can be used for supervised, unsupervised as well as
reinforcement machine learning. it uses a variety of ways to process
these.

37. Difference between Machine Learning
and Deep Learning :
Machine learning
Apply statistical algorithms to learn the
hidden patterns and relationships in the
dataset.
Deep Learning
Uses artificial neural network
architecture to learn the hidden patterns
and relationships in the dataset.
Can work on the smaller amount of
dataset
Requires the larger volume of dataset
compared to machine learning
Better for the low-label task.
Better for complex task like image
processing, natural language processing,
etc.
Takes less time to train the model. Takes more time to train the model.
A model is created by relevant features
which are manually extracted from
Relevant features are automatically
extracted from images. It is an end-to-

38. Book Reference
38
S. No. Title of Book Authors Publisher
Reference Books
T1 Pattern Recognition and Machine
Learning
Christopher M. Bishop Springer
T2 Introduction to Machine Learning
(Adaptive Computation and
Machine Learning),
Ethem Alpaydın MIT Press
Text Books
R1 Machine Learning Mitchell. T, McGraw Hill