A prevalent practice in recommender systems consists in averaging item embeddings to represent users or higher-level concepts in the same embedding space. This paper investigates the relevance of such a practice. For this purpose, we propose an expected precision score, designed to measure the consistency of an average embedding relative to the items used for its construction. We subsequently analyze the mathematical expression of this score in a theoretical setting with specific assumptions, as well as its empirical behavior on real-world data from music streaming services. Our results emphasize that real-world averages are less consistent for recommendation, which paves the way for future research to better align real-world embeddings with assumptions from our theoretical setting.

翻译：推荐系统中的一种常见做法是将物品嵌入进行平均，以在相同嵌入空间中表示用户或更高层次的概念。本文研究了这一做法的相关性。为此，我们提出了一种期望精度得分，旨在衡量平均嵌入相对于用于构建它的物品的一致性。随后，我们在具有特定假设的理论背景下分析了该得分的数学表达式，以及其在音乐流媒体服务真实数据上的经验行为。我们的结果强调，真实世界中的平均嵌入在推荐中的一致性较低，这为未来研究更好地将真实世界嵌入与理论背景的假设对齐铺平了道路。