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

Sliding Spectrum Decomposition for Diversified Recommendation

Neural text generation models are likely to suffer from the low-diversity
problem. Various decoding strategies and training-based methods have been
proposed to promote diversity only by exploiting contextual features, but
rarely do they consider incorporating syntactic structure clues. In this work,
we propose using linguistic annotation, i.e., part-of-speech (POS), to guide
the text generation. In detail, we introduce POS Guided Softmax to explicitly
model two posterior probabilities: (i) next-POS, and (ii) next-token from the
vocabulary of the target POS. A POS Guided Sampling strategy is further
proposed to address the low-diversity problem by enriching the diversity of
POS. Extensive experiments and human evaluations show that, compared with
existing state-of-the-art methods, our POS Guided Softmax and Sampling (POSG)
can generate more diverse text while maintaining comparable quality.

本研究提出了一种基于语言标注（part-of-speech）的神经文本生成模型，使用 POS Guided Softmax 和 Sampling 方法显著提高了文本多样性。