Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture

We propose an encoder-decoder neural architecture to model user session and intent using browsing and purchasing data from a large e-commerce company. We begin by identifying the source-target transition pairs between items within each session. Then, the set of source items are passed through an encoder, whose learned representation is used by the decoder to estimate the sequence of target items. Therefore, as this process is performed pair-wise, we hypothesize that the model could capture the transition regularities in a more fine grained way. Additionally, our model incorporates an attention mechanism to explicitly learn the more expressive portions of the sequences in order to improve performance. Besides modeling the user sessions, we also extended the original architecture by means of attaching a second decoder that is jointly trained to predict the purchasing intent of user in each session. With this, we want to explore to what extent the model can capture inter session dependencies. We performed an empirical study comparing against several baselines on a large real world dataset, showing that our approach is competitive in both item and intent prediction.