SPADE：逃离流行度-相似度前沿以衡量惊喜推荐

中文摘要 AI 辅助

推荐系统设计惊喜度以促进主动探索并打破可预测的消费循环。现有的离线超越准确性指标的问题在于，它们往往要么孤立地考虑历史相似度，要么孤立地考虑全局流行度。我们的目标是设计一种评估指标，同时考察相似度、流行度和实际用户相关性。为实现这一目标，我们引入了SPADE（惊喜帕累托距离评估）。SPADE将所有项目映射到二维空间，直接计算用户特定的帕累托前沿，该前沿由最大流行度和历史相似度的项目构成。最终的惊喜度得分通过计算严格针对正确推荐的测试集项目到该边界的平均最小欧几里得距离得出。在五个数据集和五个基线算法上评估SPADE证实了其有效性；我们的结果表明，该指标成功阻止了算法利用不相关或非个性化的推荐来钻超越准确性指标的漏洞，可靠地隔离出惊喜发现。

英文摘要

Recommender systems engineer serendipity to foster active exploration and break predictable consumption cycles. The problem with existing offline beyond-accuracy metrics is that they often either isolate historical similarity or global popularity. We aim to design an evaluation metric that examines similarity, popularity, and actual user relevance. To achieve this, we introduce SPADE (Serendipitous Pareto Distance Evaluation). SPADE maps all items into a two-dimensional space to directly calculate a user-specific Pareto frontier of maximally popular and historically similar items. The final serendipity score is then computed by averaging the minimum Euclidean distance from this boundary strictly for the correctly recommended test-set items. Evaluating SPADE across five datasets and five baseline algorithms confirms its effectiveness; our results show that the metric successfully prevents algorithms from exploiting beyond-accuracy measures with irrelevant or non-personalized recommendations, reliably isolating serendipitous discoveries.

发表机构

• University of Antwerp（安特卫普大学）
• University of Helsinki（赫尔辛基大学）

机构由 AI 辅助整理，请以论文原文为准。