论文标题
从排名列表到轮播：轮播点击模型
From Ranked Lists to Carousels: A Carousel Click Model
论文作者
论文摘要
基于旋转木马的推荐接口使用户可以以结构化，高效且视觉上吸引人的方式探索推荐的项目。这使他们成为了许多现实生活推荐人中最终用户推荐物品的事实标准方法。在这项工作中，我们尝试使用\ emph {Carousel Click Model}来解释轮播推荐器的效率，这是一种与基于Carousel的推荐界面的用户交互的生成模型。我们在分析和经验上研究了该模型。我们的分析结果表明，由于结构化的浏览方式，用户可以比单个排名列表中的单击列表中检查更多的项目。这些结果得到了一系列实验的支持，在其中，我们将Carousel点击模型与基于矩阵分解的推荐人集成在一起。我们表明，组合的推荐人在持有的测试数据上表现良好，并且与传统的单个排名列表相比，参与建议更高。
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.
