jester_recommender_sys
Recommendation system for the jester (joke) database using collaborative filtering and K-means clustering algorithms.
Author: Abhijith Rajeev (Abe). Project date: Jan 2017. Libraries and Code references: Introduction to Machine Learning - Andrew Ng.
************ Project Overview ******************
● The jester database has the ratings of 100 jokes from 73,421 users. Link to the datset- (http://eigentaste.berkeley.edu/dataset/) . ● Ratings of 1000 users are considered for convenience. New user ratings are appended to the data. ● Parameter learning and feature learning is performed using advanced optimization algorithms (ex: fmincg). ● Polynomial regression is performed (as a part of collaborative filtering algorithm) to predict the ratings. ● All the jokes with similar features using the K Nearest Neighbors - K means Clustering algorithm, so that the jokes can be taken from certain groups to give to the users.