Eye tracking in recommender systems can provide an additional source of implicit feedback, while helping to evaluate other sources of feedback. In this study, we use eye tracking data to inform a collaborative filtering model for movie recommendation providing an improvement over the click-based implementations and additionally analyze the area of interest (AOI) duration as related to the known information of click data and movies seen previously, showing AOI information consistently coincides with these items of interest.

翻译：眼动追踪在推荐系统中可提供额外的隐式反馈来源，同时有助于评估其他反馈来源。本研究利用眼动追踪数据构建协同过滤模型进行电影推荐，相较于基于点击的实现方法取得了改进，并进一步分析了感兴趣区域（AOI）注视时长与已知点击数据及先前观看电影信息的关联性，表明AOI信息始终与这些感兴趣项目保持一致。