Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability

International Conference on Machine Learning; ICML, 2021

Sarah Dean*, Mihaela Curmei*, Benjamin Recht https://arxiv.org/pdf/2107.00833.pdf

In this work, we consider how preference models in interactive recommendation systems determine the availability of content and users’ opportunities for discovery. We propose an evaluation procedure based on stochastic reachability to quantify the maximum probability of recommending a target piece of content to an user for a set of allowable strategic modifications.

Stochastic reachability can be used to detect biases in the availability of content and diagnose limitations in the opportunities for discovery granted to users.

We show that this metric can be computed efficiently as a convex program for a variety of practical settings, and further argue that reachability is not inherently at odds with accuracy.

We demonstrate evaluations of recommendation algorithms trained on large datasets of explicit and implicit ratings. Our results illustrate how preference models, selection rules, and user interventions impact reachability and how these effects can be distributed unevenly.


See our GitHub repository for detailed experiments.

Mihaela Curmei and Sarah Dean contributed equally to this work.