Evaluation of Recommender System Utvärdering Av Rekommendationssystem Evaluation of Recommender System Utvärdering Av Rekommendations- System

Abstract

Recommender System (RS) has become one of the most important component for many companies, such as YouTube and Amazon. A recommender system consists of a series of algorithms which predict and recommend products to users. This report covers the selection of many open source recommender system projects, and movie predictions are made using the selected recommender system. Based on the predictions, a comparison was made between precision and an improved precision algorithm. The selected RS uses singular value decomposition in the field of collaborative filtering. Based on the recommendation results produced by the RS, the comparison between precision and the improved precision algorithms showed that the result of improved precision is slightly higher than precision in different cutoff values and different dimensions of eigenvalues. Preface This report covers a thesis work in the third year in computer engineering, program and system development at School of Technology and Health, KTH. The goal of this thesis work is to create a prototype of recommendation system for PlayPilot. The report is written assuming the readers have the fundamental knowledge of statistics , linear algebra and programming concepts. I would like to thank PlayPilot to give me the opportunity to do this fascinating project. My supervisors at PlayPilot, Emil Wikströ m and Gustaf Sjö berg, the CEO of PlayPilot Adam Chrigströ m. In addition, I would like to thank my supervisor Reine Bergströ m at KTH for his patience and guide me to reach the goal using scientific methods.