Year of Graduation
Recommender Systems Based on Association Rules
Applied Mathematics and Information Science
This work is dedicated to Recommender systems, which is one of the most used approaches in machine learning. The main task is to demonstrate different approaches to building Recommender Systems, such as Association Rules and Collaborative Filtering. Also the paper proposes an algorithm which work with users who prefer rare objects. Moreover two ways of scaling data will be compared as well as performance of the algorithms depending on different parameters.