Content feed, a type of product that recommends a sequence of items for users to browse and engage with, has gained tremendous popularity among social media platforms. In this paper, we propose to study the diversity problem in such a scenario from an item sequence perspective using time series analysis techniques. We derive a method called sliding spectrum decomposition (SSD) that captures users' perception of diversity in browsing a long item sequence. We also share our experiences in designing and implementing a suitable item embedding method for accurate similarity measurement under long tail effect. Combined together, they are now fully implemented and deployed in Xiaohongshu App's production recommender system that serves the main Explore Feed product for tens of millions of users every day. We demonstrate the effectiveness and efficiency of the method through theoretical analysis, offline experiments and online A/B tests.

本文提出了一种名为"滑动谱分解"（SSD）的方法，使用时间序列分析技术从物品序列的角度研究社交媒体平台中推荐内容的多样性问题，并介绍了一种适用于长尾效应的物品嵌入方法用于准确的相似度度量。该方法已在“小红书”App的推荐系统中完全实现和部署，并通过理论分析、离线实验和在线A/B测试证明了其有效性和效率。

滑动谱分解用于多样化推荐