  Feed recommendation systems, which recommend a sequence of items for users to
browse and interact with, have gained significant popularity in practical
applications. In feed products, users tend to browse a large number of items in
succession, so the previously viewed items have a significant impact on users'
behavior towards the following items. Therefore, traditional methods that
mainly focus on improving the accuracy of recommended items are suboptimal for
feed recommendations because they may recommend highly similar items. For feed
recommendation, it is crucial to consider both the accuracy and diversity of
the recommended item sequences in order to satisfy users' evolving interest
when consecutively viewing items. To this end, this work proposes a general
re-ranking framework named Multi-factor Sequential Re-ranking with
Perception-Aware Diversification (MPAD) to jointly optimize accuracy and
diversity for feed recommendation in a sequential manner. Specifically, MPAD
first extracts users' different scales of interests from their behavior
sequences through graph clustering-based aggregations. Then, MPAD proposes two
sub-models to respectively evaluate the accuracy and diversity of a given item
by capturing users' evolving interest due to the ever-changing context and
users' personal perception of diversity from an item sequence perspective. This
is consistent with the browsing nature of the feed scenario. Finally, MPAD
generates the return list by sequentially selecting optimal items from the
candidate set to maximize the joint benefits of accuracy and diversity of the
entire list. MPAD has been implemented in Taobao's homepage feed to serve the
main traffic and provide services to recommend billions of items to hundreds of
millions of users every day.
