Search-based Interest Model (SIM) is a system for predicting user behavior given sequences of previous interactions.
SIM model consists of two components: General Search Unit (GSU) and the Exact Search Unit (ESU). The goal of the former is to filter down possibly long historical user behavior sequence to a shorter and relevant sequence. On the other hand, ESU utilizes the most recent user behaviors for a candidate item, for example, estimate click-through rate for a candidate ad. Both parts are trained jointly using data on past user behaviors.
A model architecture diagram is presented below.
Figure 1. The architecture of the model.
Embeddings in model architecture diagram are obtained by passing each feature from the dataset through the Embedding Layer. Item features from target item, short behavior history and long behavior history share embedding tables.
Figure 2. Embedding of input features.
The following datasets were used to train this model:
Performance numbers for this model are available in NGC.