The document discusses customer-to-customer (C2C) recommendation systems. It begins by describing different types of e-commerce models including business-to-business, business-to-consumer, and C2C. It then explains that C2C markets allow customers to interact and recommend products and services to one another. Recommendation systems use collaborative filtering, content-based filtering, and hybrid approaches to predict items a user may like. The document provides an example of how Facebook tracks user data for recommendations. It describes the need for a C2C recommendation system and surveys literature in the field. It outlines the system requirements, major recommendation approaches, advantages and disadvantages of each approach, and provides screenshots of an example C

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INTRODUCTION:-
There are many different classifications of marketing(E-COMMERCE)
Government to Business (G2B),
Business to Business (B2B),
 Business to Consumer (B2C),
Customer to Customer (C2C).

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INTRODUCTION:-
Customer to customer (C2C) markets provide
an innovative way to allow customers to interact
with each other
Recommendendation systems are a subclass
of information filtering system that seek to
predict the "rating" or "preference" that a user
would give to an item.
They help users discover items they might not
have found by themselves.

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AN EXAMPLE:-
THE BIGGEST SOCIAL NETWORK : FACEBOOK
Facebook tracks each and every record of the user.

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C2C RECOMMENDATION SYSTEM:-
The recommendation system which connects customer
with another customer for buying and selling is called
c2c(customer to customer) system.

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WHAT IS THE NEED ?:-

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LITERATURE SURVEY:-
In 1998, Giles et al. introduced the first research-paper
recommender system as part of the CiteSeer project. Since then,
at least 216 articles relating to 120 research-paper
recommendation approaches were published.
Yearly number of articles steadily increases: 66 of the 217
articles (30%) were published just in 2012 and 2013 alone.
Now,all great industrial giants like Facebook,Amazon,Netflix
etc.. Uses recommendation system.
It is an active research topic in the data mining and machine
learning fields.

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SYSTEM REQUIRMENT:-
SOFTWARE:-
Programming language:- php
Server :- Apache
Database :- MySql
IDE Software :- Aptana studio
HARDWARE:-
RAM:-minimum 2GB required.

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Approaches of recommendation:-
There are three major approaches for making a
recommendation system.
•Collaborative Filtering approach:-
Recommends the active user the items that the user of previous taste from
past has liked.
•Content Based Filtering:-
Recommed items similar to the ones that user liked in the past.
•Hybrid approach:-
Combination of Collaborative and Content based.

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COLLABORATIVE FILTERING:-
Its based on a model of prior user behavior. The model can be
constructed solely from a single user's behavior or — more
effectively — also from the behavior of other users who have
similar traits.
Saif Khayam

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DISADVANTAGES:-
Early rater problem:- Collaborative systems cannot provide
recommendations for new items since there are no user ratings on which to
base a prediction. Even if users start rating the item it will take some time
before the item has received enough ratings in order to make accurate
recommendations. Similarly, recommendations will also be inaccurate for
new users who have rated few items.
Sparsity problem:- In many information domains the existing number
of items exceeds the amount a person is able (and willing) to explore by far.
This makes it hard to find items that are rated by enough people on which to
base predictions.
. Gray sheep:- Groups of users are needed with overlapping
characteristics. Even if such groups exist, individuals who do not consistently
agree or disagree with any group of people will receive inaccurate
recommendations.

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CONTENT BASED FILTERING:-
This model is based upon the contents of the system. This
approach might use historical browsing information.

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DISADVANTAGES:-
Content description:- In some domains generating a useful description
of the content can be very difficult. In domains where the items consist of
music or video for example a representation of the content is not always
possible with today’s technology.
Over-specialization:- A content-based filtering system will not select
items if the previous user behavior does not provide evidence for this.
Additional techniques have to be added to give the system the capability to
make suggestion outside the scope of what the user has already shown interest
in.
Subjective domain problem:- Content-based filtering techniques
have difficulty in distinguishing between subjective information such as
points of views and humor.

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USER VS ITEM FILTERING:-
This model is based upon the contents of the system. This
approach might use historical browsing information.

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HYBRID FILTERING:-
Hybrid Filtering is a combination of both Collaborative
Filtering and Content Based Filtering.
It overcomes all the disadvantages of both the Filtering
Techniques.

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PHP:-
PHP is a server-side scripting language designed
primarily for web development.
Originally created by Rasmus Lerdorf in 1994.
 PHP originally stood for Personal Home
Page,but it now stands for the recursive
acronym PHP: Hypertext Preprocessor.
It is object oriented ,simple programming
language.
About 51% of web apps on internet is based on
PHP.

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MySql:-
MySQL is an open-source relational database management
system(RDBMS).
MySQL is written in C and C++. Its SQL parser is written
in yacc.
 MySQL is used in many high-profile, large-
scale websites,including Google, Facebook,Twitter,Flickr,and Yo
uTube.

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PHP with MySql:-
With PHP, you can connect to and manipulate databases.
PHP 5 and later can work with a MySQL database using:
•MySQLi extension (the "i" stands for improved),
•PDO (PHP Data Objects).

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OUR APP USING HYBRID FILTERING:-
It is a web Application for students to Buy and Sell their
college items to their college students.
The Application recommends the appropriate college
products to the appropriate buyer (whose chances of buying
that product is more).
The seller can see the details of the buyer to get the
product.
It basically creates a Hybrid c2c Recommendation
medium for Buying and Selling

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EXISTING SYSTEM:-
One great example of such application is OLX.
Also developed using PHP and Mysql.
It shows product near user irrespective of its relation with
user.
No Recommendation system.

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PROPOSED SYSTEM:-
It is a recommendation system.
Recommends appropriate products to more likely to buy
users.
Uses Hybrid Filtering Data mining technique for
Recommending of items to users.
Right things for Right people.

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MODULES:-
 Register:-User should register in the application.
 Login:- User enter the profile using name and
password.
 Sell:-User can put the college item for sale.
 Buy:-User can see the details of the Seller and contact
him to buy.
 Recommend:-Appropriate products are reommended.

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MODULES:-
 Cart:-User can add products to the cart.
 Like:- User can like a product.
 Notify:-User send a message to the Seller.
 Account:-User edit their details in the account section.
 Admin:-Admin can delete the UnAppropriate user or
the product.
 Search:-User can search for a product.

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CLASS DIAGRAM:-

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USECASE DIAGRAM:-

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SEQUENCE DIAGRAM:-

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REGISTER:-
 User data is stored
in the database.
 Data is then used for
login and
recommending.

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LOGIN:-
 Entered details is
matched with user
info stored in
database.
 If the data matches
the user is diaplayed
the home page.

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SELL:-
 User fills the sale
form with an image
and submits it.
 The product is then
stored in the
database with the
image.

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BUY:-
 The user selects its
respective branch to
buy.
 All the products
related to that
branch will be
displayed with seller
info.

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CART:-
 The user selects its
respective branch to
buy.
 The selected products
can be added to the
cart

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LIKE:-
 The user selects its
respective branch to
buy.
 The user can Like and
item.
 The liked items can be
seen in liked section

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Account:-
 The user can select its
details and edit it.

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Notify:-
 User can send a
notification to the seller
for buying of item.
 The messages can be seen
in the account section

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Admin:-
 Admin can delete all the
unaproppriate items and
users from the database.

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SEARCH:-
 User search for a product.
 The searched product will
be saved in database which
will be used for
recommendation.

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RECOMMEND:-
 The every action of user is
noticed and stored in
database.
 The info is then passed
through the Filtering
Algorithms.
 The result of algorithms is
then displayed to user.

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Recommendation Diagram:-

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OUTPUT SCREENSHOTS:-

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OUTPUT SCREENSHOTS:-

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OUTPUT SCREENSHOTS:-

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OUTPUT SCREENSHOTS:-

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OUTPUT SCREENSHOTS:-

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OUTPUT SCREENSHOTS:-

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OUTPUT SCREENSHOTS:-

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OUTPUT SCREENSHOTS:-

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OUTPUT SCREENSHOTS:-

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OUTPUT SCREENSHOTS:-

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ADVANTAGES:-
Based on Real Activity:-The biggest benefit of recommendation systems is that
they record, and then base their recommendations on actual user behavior.
Great for Discovery:- they allow us to discover things that are similar to what
we already like.
Personalization:-We often take recommendations from friends and family
because we trust their opinion
Always Up-To-Date:-Recommendation systems are dynamically updated, and
therefore are always up-to-date
Reduced Organizational Maintenance:- With recommendation systems, much
of this organizational maintenance goes away.

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DISADVANTAGES:-
Difficult to Set Up:-Recommendation systems are intensive,
database-driven applications that are not trivial to create and get
running.
Maintenance Shifted Elsewhere:-Even though recommendation
systems can reduce organizational maintenance, they don’t get rid of
maintenance itself.
Sometimes They’re Wrong:-Recommendation systems aren’t just
a technological challenge. They’re also a social one. Sometimes
people are unhappy with recommendations.

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CONCLUSION:-
The c2c Recommendation system is a branch of Datamining which
recommends the user ,items which the might prefer.
The hybrid Filtering is used for recommendation of items.
The user can sell and buy his college item to their college mates.
The app uses hybrid algorithms to display the user appropriate
products.
This system helps the user for discovery of new products hence
increasing the sales.

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BIBLIOGRAPHY:-
http://www.w3schools.com/php/php_mysql_connect.asp
http://recommender-systems.org/hybrid-recommender-systems/
https://articles.uie.com/recommendation_systems/
https://en.wikipedia.org/wiki/Recommender_system

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THANK YOU
(^_^)
One last thing,
Did you know presentation is a two way
communication !!
You have to ask question ..