电影上映引发原著阅读量激增，推荐系统能否及时捕捉？
Book Readership During Movie Releases: An Exploratory Analysis
- 用好读网数据匹配电影上映时间，分析改编书的阅读变化
- 电影上映月阅读量显著上升，峰值比平时高约3.5倍
- 现有推荐模型对热门改编书的响应滞后，难以实时调整排序
外部事件会暂时改变推荐系统中项目的相关性，但这种变化通常在用户行为发生后才体现在历史数据中。在图书推荐中，电影改编是典型例子：基于书籍改编的电影上映会临时提升原著的关注度和相关性。本文利用大规模好读网（Goodreads）数据，将其与电影上映日期匹配，分析这一现象。研究发现，电影上映当月读者阅读量出现明显峰值，较平时高出约3.5倍；随后评估了现有推荐模型在电影上映前后对改编书籍的排序表现。结果表明，主流模型对这类突发热度的响应存在延迟，未能及时反映读者兴趣的快速转移。
原文摘要 · Abstract (English)
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.
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