  Users of industrial recommender systems are normally suggesteda list of items
at one time. Ideally, such list-wise recommendationshould provide diverse and
relevant options to the users. However, in practice, list-wise recommendation
is implemented as top-N recommendation. Top-N recommendation selects the first
N items from candidates to display. The list is generated by a ranking
function, which is learned from labeled data to optimize accuracy.However,
top-N recommendation may lead to suboptimal, as it focuses on accuracy of each
individual item independently and overlooks mutual influence between items.
Therefore, we propose a personalized re-ranking model for improving diversity
of the recommendation list in real recommender systems. The proposed re-ranking
model can be easily deployed as a follow-up component after any existing
ranking function. The re-ranking model improves the diversity by employing
personalized Determinental Point Process (DPP). DPP has been applied in some
recommender systems to improve the diversity and increase the user
engagement.However, DPP does not take into account the fact that users may have
individual propensities to the diversity. To overcome such limitation, our
re-ranking model proposes a personalized DPP to model the trade-off between
accuracy and diversity for each individual user. We implement and deploy the
personalized DPP model on alarge scale industrial recommender system.
Experimental results on both offline and online demonstrate the efficiency of
our proposed re-ranking model.
