Abstract
Sequential recommendation is a task that learns a
temporal dynamic of a user behaviour in sequential data and predicts items that a user would like
afterward. However, diversity has been rarely emphasized in the context of sequential recommendation. Sequential and diverse recommendation must
learn temporal preference on diverse items as well
as on general items. Thus, we propose a sequential
and diverse recommendation model that predicts a
ranked list containing general items and also diverse items without compromising significant accuracy. To learn temporal preference on diverse items
as well as on general items, we cluster and relocate
consumed long tail items to make a pseudo ground
truth for diverse items and learn the preference on
long tail using recurrent neural network, which enables us to directly learn a ranking function. Extensive online and offline experiments deployed on
a commercial platform demonstrate that our models significantly increase diversity while preserving
accuracy compared to the state-of-the-art sequential recommendation model, and consequently our
models improve user satisfaction