Feature analysis for recommender systems using transformer-based architectures

Recommender systems are technology-based solutions that assist users by suggesting relevant items among millions of items. It could be anything like a movie, a meal, a vacation spot, shoes, or a piece of music. Unlike traditional recommender systems, sequential and session-based recommender systems make recommendations by paying attention to the order of items that users interact with. The advantage of such systems is that they take into account varying tastes. Additionally, due to some legal requirements, the users' data cannot be collected from some platforms, and the recommender system has to suggest the session's information without having any previous knowledge. It may only have to recommend products according to a few interactions in that session. These reasons constitute the importance of sequential and session-based recommender systems. In this thesis, we have experimented with sequential and session-based recommender systems using the Transformers4rec framework, which allows us to use transformer architectures in recommender systems. We observed that transformer architectures work better in short interaction sequences than long ones. We showed that additional features enhance the model's performance, particularly time-based features. Additionally, we examined and interpreted that the importance of features changes according to the size, shape, and type of data.