分析书评平台十年数据，揭示推荐系统行为随时间变化规律
User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation
- 用滚动窗口训练协同过滤模型，追踪推荐效果随时间演变
- 发现2011年引入算法推荐后用户活跃度与多样性显著提升
- 为时序评估设计提供数据支持，适合研究推荐系统演化者参考
数据是研究推荐系统的核心资源。尽管已有大量工作致力于改进和评估先进模型，并衡量推荐结果的多种属性，但对数据本身及其随时间演变的关注仍不足。此类分析可为推荐系统的设计与评估提供重要上下文，尤其对依赖时间划分的评估方法。本文基于从2006年起运营的Goodreads平台抓取的UCSD Book Graph数据集，开展时序解释性分析。通过活动度、多样性与公平性指标量化书籍交互数据；在滚动训练窗口上训练多组协同过滤算法，观察推荐表现随时间的变化。此外，探究2011年引入算法推荐后用户与系统行为是否出现可测量变化。
原文摘要 · Abstract (English)
Data is an essential resource for studying recommender systems. While there has been significant work on improving and evaluating state-of-the-art models and measuring various properties of recommender system outputs, less attention has been given to the data itself, particularly how data has changed over time. Such documentation and analysis provide guidance and context for designing and evaluating recommender systems, particularly for evaluation designs making use of time (e.g., temporal splitting). In this paper, we present a temporal explanatory analysis of the UCSD Book Graph dataset scraped from Goodreads, a social reading and recommendation platform active since 2006. We measure the book interaction data using a set of activity, diversity, and fairness metrics; we then train a set of collaborative filtering algorithms on rolling training windows to observe how the same measures evolve over time in the recommendations. Additionally, we explore whether the introduction of algorithmic recommendations in 2011 was followed by observable changes in user or recommender system behavior.
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