A recommender system for WordPress themes using item-based collaborative filtering technique

Abstract

Recommender systems allow making personalized recommendations for items or products while browsing online, using numerous algorithms and techniques to predict and recommend potentially useful items to a specific user. This paper presents a system built for the purpose of filtering out WordPress themes trough the ratings or reviews of other similar users using item-based collaborative filtering technique and provide user-specific recommendations for the largest online marketplace of web templates called Themeforest, part of Envato marketplaces.