A Stock-movement Aware Approach for Discovering Investors' Personalized Preferences in Stock Markets

It is very useful to endow machines with the ability to understand users' personalized preferences. In this paper, we propose a novel methodology for discovering investors' personalized preferences in stock markets. Our work is able to estimate investors' personalized preferences for each stock and thus helpful for realizing investment recommendation, for instance through recommending real-time news or others' opinions on stocks preferred by the target user. Compared to conventional approaches, our method effectively incorporates stock movements for estimating investors' preference. By capturing stock movement patterns influencing users' preferences, our method can find users with a similar investment philosophy and then increase the effect of preference prediction. An experimental evaluation with two real-world datasets demonstrates the effectiveness of our approach.