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1. GURU NANAK INSTITUTIONS TECHNICAL CAMPUS
Seminar Presentation
on :
Recommendation System Using different Machine
Learning Techniques
By:
Pvs Karthik
CSE-4

2. Content:
 Introduction
 Objective
 System Model
 Types of Recommendation System
 ML Techniques
 Benefits
 Applications
 Examples
 Conclusion

3. INTRODUCTION
• ONLINE commercial centre make their very own benefit dependent on their notices while business metric
has the business enthusiasm to rank higher on suggestions to draw in more user. Traditional retail can
present only popular products, but online can present a variety of products.
• Due to the enormous information available across the web, it is difficult for users to comprehend whether
the items presented by recommender frameworks are accurate or not.
• A recommender system extracts the user interest from the related dataset and provides quality
recommendations for the big dataset. Recommender systems are a significant part of E-Commerce that
use machine learning and data mining techniques to filter the unseen information and predict whether the
user would like a particular item.
• An intelligent system is a special type of recommender system used to exploit the historical user ratings
on data that comes from mined-relevant data through the data mining process.

4. OBJECTIVE:
 The objective of recommender systems is to provide recommendations based on recorded information
on the users' preferences. These systems use information filtering techniques to process information
and provide the user with potentially more relevant items.
 Recommender systems are widely used in several different domains for the recommendation of
articles, music, movies, and even people. Portals such as Amazon uses recommender systems to
suggest products to their customers. Meanwhile, social networks such as LinkedIn and Facebook use
them to suggest new contacts.

5. Recommendation system Model:

6. Recommender System Types:
 Collaborative/Social-filtering system :Aggregation of consumers’ preferences and recommendations to
other users based on similarity in behavioral patterns.
 Content-based system : Supervised machine learning used to induce a classifier to discriminate between
interesting and uninteresting items for the user.
 Knowledge-based system : knowledge about users and products used to reason what meets the user’s
requirements, using discrimination tree, decision support tools, case-based reasoning (CBR).
 Relevance feedback : positive/negative prototypes.
 Feature selection : Removal of non-informative terms.
 Learning to recommend : Agent counts with 2 matrices; user vs. category matrix (for successful
classification) and user’s recommendation factor (1 to 5) or binary.

7. Machine Learning Techniques:
• There are a number of different machine learning algorithms that can be used in a
recommender system. Each algorithm has its own strengths and weaknesses, and the best
algorithm for a particular application will depend on the nature of the data. The most
common is the linear regression algorithm.
• machine learning algorithms that can be used in Recommender Systems, include some of
the following:
1. Neural networks
2. K-NearestNeighbor (K-NN)
3. SPM
4. Dimentionality Reduction

8. 1.Neural Networks:
A neural network is a type of machine learning algorithm that is similar to the brain. It is composed of interconnected
neurons that can learn to recognize patterns. Neural networks are often used for prediction tasks, like recommender
systems.
2.K-NearestNeighbor (K-NN):
The K-NN algorithm is often used for recommender systems because it is able to handle large amounts of data and
can produce good predictions. The K-NN algorithm works by finding the k nearest neighbours of a given item. The
neighbours are then used to vote on the rating of the item. The algorithm then uses the average of the votes to predict
the rating of the item. The K-NN algorithm is often used for Recommender Systems because it is able to handle large
amounts of data and can produce good predictions.
3.SPM(Sequencial pattern mining):
Sequential pattern mining is a topic of Data mining concerned with finding statistically relevant patterns between data
examples where the values are delivered in a sequence.It is usually presumed that the values are discrete, and
thus mining is closely related, but usually considered a different activity. Sequential pattern mining is a special case
of structured data mining.
4.Dimentionality Reduction:
Dimensionality reduction is a type of machine learning algorithm that is used to reduce the number of dimensions in a
data set. It is often used in Recommender Systems because it can help to reduce the amount of data that needs to be
processed. The dimensionality reduction algorithm works by finding a lower dimensional representation of the data.
This can be done by using techniques like Principal Component Analysis .

9. Benefits Of Recommendation System

10. System Architure:

11. Applications:
1. E-Commerce:
Is an industry where recommendation systems were first widely used. With millions of
customers and data on their online behavior, e-commerce companies are best suited to generate
accurate recommendations.
2. Retail:
Shopping data in a store is the most valuable data as it is the most direct data point on a
customer’s intent. Retailers with troves of shopping data are at the forefront of companies making
accurate recommendations.
3. Media:
Similar to e-commerce, media businesses are one of the first to jump into recommendations.
It is difficult to see a news site without a recommendation system.

12. 4. Banking:
A mass-market product that is consumed digitally by millions. Banking for the masses and
SMEs are prime for recommendations. Knowing a customer’s detailed financial situation, along with
their past preferences, coupled with data of thousands of similar users, is quite powerful.
5. Telecom:
It Shares similar dynamics with banking. Telcos have access to millions of customers whose
every interaction is recorded. Their product range is also rather limited compared to other industries,
making recommendations in telecom an easier problem.
6. OTT Platform:
Based on the user watching data and given user preferences the Recommendation system tries
to suggest some movies and series for the user .It also helps in classification of data based on the
categories of the movies and series like top rated ,mostly watched ,etc.

13. Examples Of Applications that uses
Recommendation engine:
1. E-Commerece Websites
2. Netflix
3. Spotify
4. Banks
5. YouTube
6. Social media platforms
7. Stock Trading support ssytems

14. CONCLUSION:
The primary goal is to provide recommendations to the user in a e-commerce website by making use of
machine learning algorithms. The dataset considered has the ratings given by the other users to a specific product
and depending on the between the rated product System tries to recommend the products to our current user.With
this the system improve the efficiency of the system. And it should also be able to give appropriate
recommendations to the users who don’t have any previous purchase history or to the new users.
We can also work on providing sub-optimal recommendations to the user and record the reaction of the
user and it can be used in the future by the system..