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.

翻译：金融科技的兴起将金融服务转移至在线平台，然而与其他行业相比，股票投资推荐系统受到的关注相对有限。个性化股票推荐能够显著提升该行业的客户参与度和满意度。然而，传统投资推荐侧重于基于现代投资组合理论的高收益股票或高度分散的投资组合，往往忽略了用户偏好。另一方面，协同过滤（CF）方法也可能无法直接适用于股票推荐，因为仅推荐用户喜爱的股票并不合适。关键在于将用户偏好与投资组合理论进行最优融合。然而，在推荐系统领域中，关于股票推荐的研究仍然相对有限，且现有模型未同时考虑用户偏好与股票的风险收益特征。为此，我们提出了一种均值-方差有效协同过滤（MVECF）模型，用于同时兼顾这两方面的股票推荐。该模型通过系统性处理股票价格的不确定性，旨在改进风险（收益方差）与收益（均值收益）之间的权衡中的帕累托最优性（均值-方差有效性）。这些改进通过正则化方法整合到MVECF模型中，并重构模型以适应普通矩阵分解框架，从而提升计算效率。基于真实基金持仓数据的实验表明，我们的模型在仅牺牲少量平均精度和召回率的情况下，能够提升建议投资组合的均值-方差有效性。最后，我们进一步展示了MVECF易于应用于基于图的最先进排序模型。