ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation

Understand

A user can be represented as what he/she does along the history.

• A common way to deal with the user modeling problem is to manually extract all kinds of aggregated features over the heterogeneous behaviors, which may fail to fully represent the data itself due to limited human instinct.
• Recent works usually use RNN-based methods to give an overall embedding of a behavior sequence, which then could be exploited by the downstream applications.
• However, this can only preserve very limited information, or aggregated memories of a person.