Predicting purchasing intention through a single stage siamese deep learning models - Takano, Kevin Kimiya (2022)
Understanding consumer buying behavior in the context of e-commerce is a recent trend at large retail stores. It can be very attractive for retail companies to know which users will buy in their market and what products they will buy. Through the study of online user behavior, models can be created to improve marketing personalizing, and build digital products. Through the historical data of user events, such as clicked items, it is possible to use them as resources to forecast purchases. Despite how valuable is this data, it is not so simple to create machine learning models using them. A very small number of user sessions are buyers of items, and, in general, models have greater learning difficulties with unbalanced classes. Moreover, there are a large number of products in a store, making the problem even more complex. Previous works in the literature show that it is better to solve the problem in two stages, i.e., using two models: one model to predict which customers will be buying items and another to predict which products will be purchased among these consumers. Solving problems in two stages makes the problem simpler since it divides the model's complexity. However, creating two models the second model does not use information from non-buyer-sessions to solve the item classification. Furthermore, if the first model fails to classify a session as a buyer-session, the second model may have its results negatively impacted. Therefore, for this work, our objective is to develop a model that solves the problem with just a single model, a single-stage model. We deployed Siamese neural networks to extract features to deal with imbalances. In our single-stage framework, we had several contributions. First, is the creation of a new loss function, the quartet-loss, which optimizes the parameters differently from the triplet-loss. Second, is the development of two different strategies for modeling user click sessions. Third, is the creation of metrics that evaluate the results of e-commerce models in online sessions. And finally, we developed machine learning methods using this project framework that reached the state-of-the-art for this problem.