| dc.description.abstract | The recommender system is becoming ubiquitous in the era when an overwhelming amount of
information surrounds people. Utilizing effective recommendations has been shown to enhance
business and make it simpler for customers to choose from an endless list of options. Especially
on e-commerce websites, where many products we buy online are suggested to us by
recommender systems. This thesis focuses on an approach known as sequential
recommendations, which leverages historical interactions in chronological order to discover
user preferences and predict the most referenced products based on the user’s historical
behaviour sequence. The applied deep learning models are the Self-attentive Sequential
Recommendation (SASRec) and Sequential Recommendation with Bidirectional Encoder
Representations from Transformer (BERT4Rec). The performance of the recommender system
was evaluated using Amazon Books dataset and measured with ranking and accuracy metrics.
Moreover, the model has also been implemented in a web prototype to provide real-time book
recommendations to the user and demonstrate practical use cases of the model in the context of
an e-commerce site. | en_US |
