  Recommender systems are essential for content-sharing platforms by curating
personalized content. To evaluate updates to recommender systems targeting
content creators, platforms frequently rely on creator-side randomized
experiments. The treatment effect measures the change in outcomes when a new
algorithm is implemented compared to the status quo. We show that the standard
difference-in-means estimator can lead to biased estimates due to recommender
interference that arises when treated and control creators compete for
exposure. We propose a "recommender choice model" that describes which item
gets exposed from a pool containing both treated and control items. By
combining a structural choice model with neural networks, this framework
directly models the interference pathway while accounting for rich
viewer-content heterogeneity. We construct a debiased estimator of the
treatment effect and prove it is $\sqrt n$-consistent and asymptotically normal
with potentially correlated samples. We validate our estimator's empirical
performance with a field experiment on Weixin short-video platform. In addition
to the standard creator-side experiment, we conduct a costly double-sided
randomization design to obtain a benchmark estimate free from interference
bias. We show that the proposed estimator yields results comparable to the
benchmark, whereas the standard difference-in-means estimator can exhibit
significant bias and even produce reversed signs.
