  Recommendation systems have become ubiquitous in today's online world and are
an integral part of practically every e-commerce platform. While traditional
recommender systems use customer history, this approach is not feasible in
'cold start' scenarios. Such scenarios include the need to produce
recommendations for new or unregistered users and the introduction of new
items. In this study, we present the Purchase Intent Session-bAsed (PISA)
algorithm, a content-based algorithm for predicting the purchase intent for
cold start session-based scenarios. Our approach employs deep learning
techniques both for modeling the content and purchase intent prediction. Our
experiments show that PISA outperforms a well-known deep learning baseline when
new items are introduced. In addition, while content-based approaches often
fail to perform well in highly imbalanced datasets, our approach successfully
handles such cases. Finally, our experiments show that combining PISA with the
baseline in non-cold start scenarios further improves performance.
