  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.
