FACEBOOK BLOCKET WITH UNSUPERVISED LEARNING FILTER

Diploma

ABSTRACT


The Internet has become a valuable channel for both business-toconsumer and business-to-business e-commerce. It has changed the way for many companies to manage the business. Every day, more and more companies are making their presence on Internet. Web sites are launched for online shopping as web shops or online stores are a popular means of goods distribution. The number of items sold through the internet has sprung up significantly in the past few years. Moreover, it has become a choice for customers to do shopping at their ease. Thus, the aim of this thesis is to design and implement a consumer to consumer application for Facebook, which is one of the largest social networking website. The application allows Facebook users to use their regular profile (on Facebook) to buy and sell goods or services through Facebook. As we already mentioned, there are many web shops such as eBay, Amazon, and applications like blocket on Facebook. However, none of them is directly interacting with the Facebook users, and all of them are using their own platform. Users may use the web shop link from their Facebook profile and will be redirected to web shop. On the other hand, most of the applications in Facebook use notification method to introduce themselves or they push their application on the Facebook pages. This application provides an opportunity to Facebook users to interact directly with other users and use the Facebook platform as a selling/buying point. The application is developed by using a modular approach. Initially a Python web framework, i.e., Django is used and association rule learning is applied for the classification of users’ advertisments. Apriori algorithm generates the rules, which are stored as a separate text file. The rule file is further used to classify advertisements and is updated regularly.

MOTIVATION AND SCOPE OF THE THESIS WORK

The scope of this thesis is to develop a web store application in social media, i.e., Facebook. The users of social media are able to advertise product to sell or search the advertised product to buy. By using the unsupervised learning algorithm, authors will make sure that selling product followed the general ethical rule.

The main motivation of this thesis is to design and implement a C2C application for the social media, which give an opportunity to authors to learn about social network, application development for social media (Facebook), e-marketing and webpage development. We have used the Facebook which is the largest online social networking website. The role of application is to allow Facebook users to buy and sell goods or services through Facebook. There are different e-Commerce applications on Facebook, e.g., eBay and Amazon, however, none of them are directly interacting with the Facebook users, and all of them are using their own platform. Most of the applications in the Facebook, use notification method or they advertise their application on the Facebook page to introduce themselves in users. Our application uses the Facebook platform and network to spread the advertisement of a particular item from a particular user among the Facebook users and enables them for buy and selling.