Tree enhanced embedding model predictive analysis methods and systems

TL;DR: In this article, a predictive analysis method consisting of receiving input data comprising an indication of a user, an indicator of an item, a user feature vector indicating features of the user and an item feature vector indicated features of items, and constructing a cross feature vector indicates values for cross features between features of users and items, is described.

Abstract: Methods and systems for predictive analysis are disclosed. A predictive analysis method comprises receiving input data comprising an indication of a user, an indication of an item, a user feature vector indicating features of the user and an item feature vector indicating features of the item; and constructing a cross feature vector indicating values for cross features between features of the user and features of the user. Embedding vectors derived from the cross feature vector, the user feature vector and the item feature vector are input into an attention network to determine a set of attentive weights which indicate cross features between the user and item features. These cross features are used in the identification of a user item preference.