  The rise of FinTech has transformed financial services onto online platforms,
yet stock investment recommender systems have received limited attention
compared to other industries. Personalized stock recommendations can
significantly impact customer engagement and satisfaction within the industry.
However, traditional investment recommendations focus on high-return stocks or
highly diversified portfolios based on the modern portfolio theory, often
neglecting user preferences. On the other hand, collaborative filtering (CF)
methods also may not be directly applicable to stock recommendations, because
it is inappropriate to just recommend stocks that users like. The key is to
optimally blend users preference with the portfolio theory. However, research
on stock recommendations within the recommender system domain remains
comparatively limited, and no existing model considers both the preference of
users and the risk-return characteristics of stocks. In this regard, we propose
a mean-variance efficient collaborative filtering (MVECF) model for stock
recommendations that consider both aspects. Our model is specifically designed
to improve the pareto optimality (mean-variance efficiency) in a trade-off
between the risk (variance of return) and return (mean return) by systemically
handling uncertainties in stock prices. Such improvements are incorporated into
the MVECF model using regularization, and the model is restructured to fit into
the ordinary matrix factorization scheme to boost computational efficiency.
Experiments on real-world fund holdings data show that our model can increase
the mean-variance efficiency of suggested portfolios while sacrificing just a
small amount of mean average precision and recall. Finally, we further show
MVECF is easily applicable to the state-of-the-art graph-based ranking models.
