Understanding User Behavior in Carousel Recommendation Systems for Click Modeling and Learning to Rank

TL;DR

This paper discusses a PhD project focused on understanding user behavior in carousel recommendation systems, proposing new click models and learning to rank methods, supported by eye tracking studies and public datasets.

Contribution

It introduces new click models and learning to rank techniques specifically for carousel recommenders, addressing gaps in understanding user browsing behavior.

Findings

Eye tracking reveals detailed browsing behavior.

Public dataset of eye tracking data will be released.

Insights inform new click models and ranking methods.

Abstract

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.