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

@inproceedings{Loyola2017ModelingUS,
  title={Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture},
  author={Pablo Loyola and Chen Liu and Yu Hirate},
  booktitle={RecSys},
  year={2017}
}
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… CONTINUE READING

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