  Carousels (also-known as multilists) have become the standard user interface
for e-commerce platforms replacing the ranked list, the previous standard for
recommender systems. While the research community has begun to focus on
carousels, there are many unanswered questions and undeveloped areas when
compared to the literature for ranked lists, which includes information
retrieval research on the presentation of web search results. This work is an
extended abstract for the RecSys 2023 Doctoral Symposium outlining a PhD
project, with the main contribution of addressing the undeveloped areas in
carousel recommenders: 1) the formulation of new click models and 2) learning
to rank with click data.
  We present two significant barriers for this contribution and the field: lack
of public datasets and lack of eye tracking user studies of browsing behavior.
Clicks, the standard feedback collected by recommender systems, are
insufficient to understand the whole interaction process of a user with a
recommender requiring system designers to make assumptions, especially on
browsing behavior. Eye tracking provides a means to elucidate the process and
test these assumptions. Thus, to address these barriers and encourage future
work, we will conduct an eye tracking user study within a carousel movie
recommendation setting and make the dataset publicly available. Moreover, the
insights learned on browsing behavior will help motivate the formulation of new
click models and learning to rank.
