Publication Details
Understanding User Behavior in Carousel Recommendation Systems for Click Modeling and Learning to Rank
Carousel-based Recommender Systems, Learning to Rank, Click Models
Although carousels (also-known as multilists) have become the standard user
interface for recommender systems in many domains (e-commerce, streaming
services, etc.) replacing the ranked list, there are many unanswered questions
and undeveloped areas when compared to the literature for ranked lists. This is
due to two significant barriers: 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.
In this extended abstract, the PhD project is outlined, which aims to address the
open research questions in carousel recommender systems by: 1) improving our
understanding of users' browsing behavior with carousels, 2) formulating a new
click model based on the empirical evidence of users' behavior, and 3) proposing
a learning to rank algorithm adapted to the carousel setting. For this purpose,
we will carry out the first eye tracking user study within a carousel movie
recommendation setting and make the resulting unique dataset of users' gaze and
clicks publicly available.
@inproceedings{BUT193279,
author="Santiago Jose {de Leon Martinez}",
title="Understanding User Behavior in Carousel Recommendation Systems for Click Modeling and Learning to Rank",
booktitle="Proceedings of the Seventeenth ACM International Conference on Web Search and Data Mining",
year="2024",
pages="1139--1141",
publisher="Association for Computing Machinery",
address="New York",
doi="10.1145/3616855.363573",
isbn="979-8-4007-0371-3",
url="https://dl.acm.org/doi/abs/10.1145/3616855.3635734"
}