A Trend-aware Investment Target Recommendation System with Heterogeneous Graph

Recent years have witnessed the booming of venture capital industry, which may not only bring huge benefits to investors, startups and emerging industries, but also lead to potential risk due to the difficulty to find proper investment targets, especially for inexperienced investors. Therefore, the investment target recommendation task has long been treated as a valuable issue to help the investors for the next investments based on their historical behavior and the market trend. However, due to the sequential features of historical invest records, which could be severely disturbed by the time-varying factors like market trend or short-term invest preference, it will be extremely difficult to accurately reveal the potential rule of investment. Moreover, the latent relationships between investors and targets (i.e., startups) or brands, which formed a heterogeneous graph to reflect the mutual influence within investors, have been largely ignored. To that end, in this paper, we propose a novel paradigm for investment target recommendation with integrating the sequential records and heterogeneous relation graph. Specifically, we aggregate information at the micro level through a time segmentation method, and then extract trends at the macro level. Meanwhile, we use relational graph convolutional networks to propagate the latent preferences through the various relationships. Based on the validations on the realworld dataset, by fusing above information, the Investment Target Recommendation System (ITRS) we proposed has achieved impressive results compared with competitive baseline methods, which indicates the significance of both sequential and structural information.