As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument.
Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations.
To the best of our knowledge, this is the first work providing an efficient neighborhood-based interaction model in the HIN-based recommendations.
These retrieved behaviors are then fed into a deep model to make the final prediction instead of simply using the most recent ones.
Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data.
In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user.
To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation.