Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prediction is very hard, especially about conversion. Predicting user
  purchases from clickstream data in fashion e-commerce   Knowing if a user is a buyer vs window shopper solely based on clickstream
data is of crucial importance for ecommerce platforms seeking to implement
real-time accurate NBA (next best action) policies. However, due to the low
frequency of conversion events and the noisiness of browsing data, classifying
user sessions is very challenging. In this paper, we address the clickstream
classification problem in the fashion industry and present three major
contributions to the burgeoning field of AI in fashion: first, we collected,
normalized and prepared a novel dataset of live shopping sessions from a major
European e-commerce fashion website; second, we use the dataset to test in a
controlled environment strong baselines and SOTA models from the literature;
finally, we propose a new discriminative neural model that outperforms neural
architectures recently proposed at Rakuten labs.

13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th