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
