Abstract: A system and method for recommendations for online e-commerce to a user, which provide a number of different methods for surfacing a current desire or need of the user in terms of a purchase, through a recommendation engine that applies one or more neural net models. One non-limiting example of such a method is to apply a first model for analyzing the current online behavior of the user, and a second model for analyzing past online behavior of the user, then concatenating the outputs of both models to a single output, to determine a recommendation for the user. The two models may be of the same or different type. For example and without limitation, the first model may comprise a CNN, while the second model may comprise a CNN and/or a transformer-based model. The transformer-based model may comprise an encoder alone, rather than the art-known combination of an encoder and a decoder. If the model comprises a CNN, preferably the CNN features a plurality of overlapping filters of different shapes.