  Carousel-based recommendation interfaces allow users to explore recommended
items in a structured, efficient, and visually-appealing way. This made them a
de-facto standard approach to recommending items to end users in many real-life
recommenders. In this work, we try to explain the efficiency of carousel
recommenders using a \emph{carousel click model}, a generative model of user
interaction with carousel-based recommender interfaces. We study this model
both analytically and empirically. Our analytical results show that the user
can examine more items in the carousel click model than in a single ranked
list, due to the structured way of browsing. These results are supported by a
series of experiments, where we integrate the carousel click model with a
recommender based on matrix factorization. We show that the combined
recommender performs well on held-out test data, and leads to higher engagement
with recommendations than a traditional single ranked list.
