User Feedback based Recommendation Engine using Neural Network

Abstract

With a paradigm shift of focus towards user behavior in today’s E-commerce driven contemporary world there is a need for an efficient and portable learning methodology for machines so as to serve a particular user or a fraternity of users collectively based on knowledge acquired on interests of a pool of users. However, any data driven or data sourced Engine can be contaminated by redundant entries or bogus feedback to jack up or scale down a particular commodity on offer so as to facilitate the promotion of another one in it’s stead. This calls for the implementation of a Neural Network which familiarizes itself with the most popular user choices by virtue of user provided ratings and Machine Learning uses a Support Vector Machine model for filtering time-stamps and IP addresses associated with each user interaction with the system to nullify attempts at manipulating said user feedback or rating by miscreants that could affect recommendation of popular choices explicitly.A Neural Network using 12 Neurons representing various dimensions of a user commodity in form a theme or a colour coded scheme is used on which a rating system is laden for user to provide feedback. This feedback is used to provide unsupervised learning. The monitoring of time-stamps and IP addresses of each user feedback is done by using a supervised learning technique. This makes the model a semi-supervised one in it’s entirety which is the best approach. The Neural Network furthermore adopts a layered learning approach where it uses the ratings provided by the users to learn which fore-ground colour contrasts best with which background colour.In this research a comprehensive study of 10 relevant papers has been made to highlight and discover the scope of research and key challenges to the already existing systems in employment by various entities to serve a similar purpose.