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Sliding Spectrum Decomposition for Diversified Recommendation
arXiv - CS - Information Retrieval Pub Date : 2021-07-12 , DOI: arxiv-2107.05204
Yanhua Huang, Weikun Wang, Lei Zhang, Ruiwen Xu

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.

中文翻译:

多样化推荐的滑动谱分解

内容提要是一种向用户推荐一系列项目供用户浏览和参与的产品,在社交媒体平台中广受欢迎。在本文中,我们建议使用时间序列分析技术从项目序列的角度研究这种场景中的多样性问题。我们推导出一种称为滑动频谱分解 (SSD) 的方法,该方法可以捕捉用户在浏览长项目序列时对多样性的感知。我们还分享了我们在设计和实施合适的项目嵌入方法以在长尾效应下进行精确相似度测量方面的经验。结合在一起,它们现在已经完全实施并部署在小红书 App 的生产推荐系统中,该系统每天为数千万用户提供主要的 Explore Feed 产品。
更新日期:2021-07-13
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