  In complex financial markets, recommender systems can play a crucial role in
empowering individuals to make informed decisions. Existing studies
predominantly focus on price prediction, but even the most sophisticated models
cannot accurately predict stock prices. Also, many studies show that most
individual investors do not follow established investment theories because they
have their own preferences. Hence, the tricky point in stock recommendation is
that recommendations should give good investment performance but also should
not ignore individual preferences. To develop effective stock recommender
systems, it is essential to consider three key aspects: 1) individual
preferences, 2) portfolio diversification, and 3) temporal aspect of both stock
features and individual preferences. In response, we develop the portfolio
temporal graph network recommender PfoTGNRec, which can handle time-varying
collaborative signals and incorporates diversification-enhancing contrastive
learning. As a result, our model demonstrated superior performance compared to
various baselines, including cutting-edge dynamic embedding models and existing
stock recommendation models, in a sense that our model exhibited good
investment performance while maintaining competitive in capturing individual
preferences. The source code and data are available at
https://anonymous.4open.science/r/IJCAI2024-12F4.
