发表机构
Drexel University; Sapienza University of Rome（德雷塞尔大学; 罗马第一大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用Goodreads数据集匹配电影上映日期，发现电影上映前后书籍阅读量有明显峰值，并评估现有推荐模型对电影改编书籍的排名情况，探索外部事件对推荐系统物品相关性的影响。
AI 中文摘要
外部事件可暂时改变推荐系统中物品的相关性，但用户作出反应后，这些变化才会在历史交互数据中显现。在书籍推荐领域，电影改编作品就是此类事件的典型例子：基于某本书改编的电影上映，会暂时提升读者对原著的关注度，并改变其对部分读者的相关性。我们将大型Goodreads数据集与电影上映日期匹配，对该现象展开研究，发现电影上映月份前后的书籍阅读量出现明显峰值；随后我们评估现有推荐模型，以了解这些模型在电影上映前后对电影改编书籍的排名情况。
英文摘要
Exogenous events can temporarily change the relevance of items in recommender systems, but these shifts are often not visible in historical interaction data until after users have already responded. In book recommendation, movie adaptations provide a clear example of such events: the release of a movie based on a book can temporarily increase attention to the source text and change its relevance for some readers. We examine this phenomenon using a large Goodreads dataset matched to movie release dates. We find a clear spike in readership around the release month, and then we evaluate existing recommendation models to understand how they rank movie-adapted books around the movie release date.
CommentsRecTemp 2026, Temporal Reasoning in Recommender Systems, Workshop at ACM RecSys 2026
