  This paper is concerned with portfolio optimization models for creating
high-quality lists of recommended items to balance the accuracy and diversity
of recommendations. However, the statistics (i.e., expectation and covariance
of ratings) required for mean--variance portfolio optimization are subject to
inevitable estimation errors. To remedy this situation, we focus on robust
optimization techniques that derive reliable solutions to uncertain
optimization problems. Specifically, we propose a robust portfolio optimization
model that copes with the uncertainty of estimated statistics based on the
cardinality-based uncertainty sets. This robust portfolio optimization model
can be reduced to a mixed-integer linear optimization problem, which can be
solved exactly using mathematical optimization solvers. Experimental results
using two publicly available rating datasets demonstrate that our method can
improve not only the recommendation accuracy but also the diversity of
recommendations compared with conventional mean--variance portfolio
optimization models. Notably, our method has the potential to improve the
recommendation quality of various rating prediction algorithms.
