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user behaviours. Also, different user behaviours such
as only viewing, adding to cart and time spending on
items can be integrated RNN based recommendation
in addition to item feature embedding and user fea-
ture embedding. In this work, we split the sessions
from different levels, and we used all items in the
first side as interaction, as seen in the experiment sec-
tion. However, instead of using all items as interacted
items, one can design a different method to analyse
the effect of inputs one by one or different input com-
binations as the interacted items from the first part of
the split session.
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