A hybrid recommender system for usage within e-commerce Content-boosted, context-aware, and collaborative filtering-based tensor factorization recommender system for targeted advertising within e-commerce

Abstract

Recommender systems are information filtering systems that try to predict what rating a user would give an item, usually with the goal of recommending, would be high rated items to users. Today there exists recommender systems in most online stores, in one form or another. The complexity of these systems varies greatly, where the less complex ones might base their recommendations on similar products, while others are much more complex, utilizing user modeling etc. This thesis describes changes made to a context-aware and collaborative filtering-based tensor factorization recommender system, in order to adapt it to perform better with the implicit-only data found in e-commerce, specifically garment-based e-commerce. Multiple contexts are evaluated in regard to a specific data set, and the performance impact of the changes proposed are also measured. The evaluation is carried out through use of self-implemented algorithms written in Python. The project resulted in a content-boosted, context-aware, and collaborative filtering-based tensor factorization recommender system made for implicit-only e-commerce data. The results show that the changes proposed in this thesis give a substantial performance increase, while time-based contexts do not seem to increase performance, in regard to the specific data set used for evaluation in this project.