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.

翻译：轮播（亦称多列表）已成为电商平台的标准用户界面，取代了排名列表——推荐系统先前采用的标准形式。尽管学术界已开始关注轮播，但与涵盖网络搜索结果呈现的信息检索研究的排名列表文献相比，仍存在诸多未解问题和发展空白。本文作为RecSys 2023博士论坛的扩展摘要，概述了一项博士课题，主要贡献在于解决轮播推荐中的发展空白：1）新型点击模型的构建，以及2）利用点击数据进行排序学习。我们指出了实现该贡献及该领域面临的两大障碍：缺乏公开数据集，以及缺少针对浏览行为的眼动用户研究。作为推荐系统收集的标准反馈，点击行为不足以理解用户与推荐系统的完整交互过程，这要求系统设计者做出假设，尤其是在浏览行为层面。眼动追踪为阐明该过程并检验这些假设提供了手段。因此，为克服这些障碍并推动未来研究，我们将在轮播电影推荐场景中开展一项眼动用户研究，并将数据集公开。此外，从浏览行为中获得的见解将为新型点击模型的构建和排序学习提供理论支撑。