  When recommending personalized top-$k$ items to users, how can we recommend
the items diversely to them while satisfying their needs? Aggregately
diversified recommender systems aim to recommend a variety of items across
whole users without sacrificing the recommendation accuracy. They increase the
exposure opportunities of various items, which in turn increase potential
revenue of sellers as well as user satisfaction. However, it is challenging to
tackle aggregate-level diversity with a matrix factorization (MF), one of the
most common recommendation model, since skewed real world data lead to skewed
recommendation results of MF. In this work, we propose DivMF (Diversely
Regularized Matrix Factorization), a novel matrix factorization method for
aggregately diversified recommendation. DivMF regularizes a score matrix of an
MF model to maximize coverage and entropy of top-$k$ recommendation lists to
aggregately diversify the recommendation results. We also propose an unmasking
mechanism and carefully designed mi i-batch learning technique for accurate and
efficient training. Extensive experiments on real-world datasets show that
DivMF achieves the state-of-the-art performance in aggregately diversified
recommendation.
