  User embeddings (vectorized representations of a user) are essential in
recommendation systems. Numerous approaches have been proposed to construct a
representation for the user in order to find similar items for retrieval tasks,
and they have been proven effective in industrial recommendation systems as
well. Recently people have discovered the power of using multiple embeddings to
represent a user, with the hope that each embedding represents the user's
interest in a certain topic. With multi-interest representation, it's important
to model the user's preference over the different topics and how the preference
change with time. However, existing approaches either fail to estimate the
user's affinity to each interest or unreasonably assume every interest of every
user fades with an equal rate with time, thus hurting the recall of candidate
retrieval. In this paper, we propose the Multi-Interest Preference (MIP) model,
an approach that not only produces multi-interest for users by using the user's
sequential engagement more effectively but also automatically learns a set of
weights to represent the preference over each embedding so that the candidates
can be retrieved from each interest proportionally. Extensive experiments have
been done on various industrial-scale datasets to demonstrate the effectiveness
of our approach.
