User Simulator
This overview presents the user simulator and its dynamic re-rankers, which are included in Informfully Recommenders. In this context, dynamic means that the re-rankers are applied multiple times per user session. They take the candidate list from the model and re-rank the items based on intra-session user feedback. To that end, the simulator allows for defining these intra-session browsing behaviors and patterns to simulate user interactions.
INFO
This tutorial outlines part of the workflow for the Informfully Recommenders repository. The Recommenders Pipeline provides an overview of all components. And you can look at the Tutorial Notebook for hands-on examples of everything outlined here.
User Simulator
Dynamic re-ranking requires an underlying user model that specifies how the item feed is being browsed. We provide a sample template that can be customized and extended. In the context of NRSs, the two default behaviors included in the framework are:
- Users are more likely to click on articles from a category that they have previously read, and
- Items higher up in the recommendation list are more likely to be clicked.
Dynamic Attribute Penalization (DAP)
DAP offers a dynamic intra-session re-ranking option that updates recommendations in response to user interaction. It diversifies the recommendation list by penalizing items in upcoming sessions that share attributes with clicked ones.