Recommendation engines help companies recommend the right products or services to customers based on their past behavior. They work by discovering patterns in customer data using machine learning and recommending items that correlate to customer interests. There are three main types: content-based filtering which recommends similar items; collaborative filtering which recommends items liked by similar users; and hybrid models which combine both approaches. Amazon and Netflix are examples of successful companies that use recommendation engines. Amazon's engine contributes significantly to its sales growth, with high conversion rates from email recommendations. Netflix's proprietary system analyzes streaming data to provide personalized movie and show recommendations to help users find new content of interest.

1. Recommendation Engine.
NAME: PRATIK KADAM`

2. CONTENTS.
 Introduction.
 What are recommendation engine?
 How recommendation engine works?
 Case study.

3. INTRODUCTION
Recommendation engines also called personalization
engines or recommendation software, help companies
recommend the right product or service to their
customers based on historical customer behavior.
To be categorized as a recommendation engine, a
product must be able to make personalized
recommendations based on customer data

4. What is recommendation Engine?
 Recommendation engines basically are data filtering tools that
make use of algorithms and data to recommend the most
relevant items to a particular user. Or in simple terms, they are
nothing but an automated form of a “shop counter guy”. You ask
him for a product. Not only he shows that product, but also the
related ones which you could buy.

6. engines discovers data patterns in the data set by learning consumers choices and produces
the outcomes that co-relates to their needs and interests.
How recommendation engines works?
 Recommendation engines discovers data patterns in the data set by learning
consumers choices and produces the outcomes that co-relates to their needs
and interests.
 A recommendation engine works using a combination of data and machine
learning technology.
 There are basically 3 types of recommendation engines.
 1.Content based filtering.
 2. Collaberative filtering.
 3. hybrid model.

7.  Content-based filtering.
 Content-based filtering works on the principle that if you like a particular item, you will also like this other item. To
make recommendations, algorithms use a profile of the customer’s preferences and a description of an item (genre,
product type, color, word length) to work out the similarity of items using cosine and Euclidean distances.

 The downside of content-based filtering is that the system is limited to recommending products or content similar to
what the person is already buying or using. It can’t go beyond this to recommend other types of products or content.
For example, it couldn’t recommend products beyond homeware if the customer had only brought homeware.

8.  Collaborative filtering.
 Collaborative filtering focuses on collecting and analyzing data on
user behavior, activities, and preferences, to predict what a person
will like, based on their similarity to other users.

 To plot and calculate these similarities, collaborative filtering uses
a matrix style formula. An advantage of collaborative filtering is that
it doesn’t need to analyze or understand the content (products,
films, books). It simply picks items to recommend based on what
they know about the user.

9. Hybrid recommendation.
 A hybrid recommendation engine looks at both the meta (collaborative) data and the transactional (content-based)
data. Because of this, it outperforms both.

 In a hybrid recommendation engine, natural language processing tags can be generated for each product or item
(movie, song), and vector equations used to calculate the similarity of products. A collaborative filtering matrix can
then be used to recommend items to users depending on their behaviors, activities, and preferences. Netflix is the
perfect example of a hybrid recommendation engine. It takes into account both the interests of the user (collaborative)
and the descriptions or features of the movie or show (content-based).

11. CASE STUDY

12. Amazon
 Judging by Amazon’s success, the recommendation system
works. The company reported a 29% sales increase to $12.83
billion during its second fiscal quarter, up from $9.9 billion
during the same time last year. A lot of that growth arguably has
to do with the way Amazon has integrated recommendations
into nearly every part of the purchasing process…”

14. Amazon
 Amazon also doles out recommendations to users via email…
In fact, the conversion rate and efficiency of such emails are
‘very high,’ significantly more effective than on-site
recommendations. According to Sucharita Mulpuru, a Forrester
analyst, Amazon’s conversion to sales of on-site
recommendations could be as high as 60% in some cases
based off the performance of other e-commerce sites.”

15. Netflix
 Netflix is a media service provider that is based out of America.
It provides movie streaming through a subscription model. It
includes television shows and in-house produced content along
with movies. Initially, Netflix used to sell DVDs and functioned
as a rental service by mail. They have discontinued selling
DVDs a year later but continued their rental service. In 2010,
they went online and started a streaming service. Since then
Netflix has grown to be one of the best and largest streaming
services in the world (Netflix,2020).

16. Netflix
 business is a subscription service model that offers
personalized recommendations, to help you find shows and
movies of interest to you. To do this we have created a
proprietary, complex recommendations system. This article
provides a high level description of our recommendations
system in plain language.
 The main motive is to provide the relevant data to the customer
who desiers to watch.

17. Thank you