Utilizing implicit feedback data to build a hybrid recommender system

Abstract

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsIn e-commerce applications, buyers are overwhelmed by the number of products due to the high depth of assortments. They may be interested in receiving recommendations to assist with their purchasing decisions. However, many recommendation engines perform poorly in the absence of community data and contextual data. This thesis examines a hybrid matrix factorisation model, LightFM, representing users and items as linear combinations of their content features’ latent factors. The model embedding item features displays superior user and item cold-start performance. The results demonstrate the importance of selectively embedding contextual data in the presence of cold-start