Facebook Blocket with Unsupervised Learning

Abstract: The Internet has become a valuable channel for both business-to- consumer 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 on-line 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 separate text file. The rule file is further used to classify advertisements and is updated regularly.