# Sliding Spectrum Decomposition for Diversified Recommendation

arXiv: https://arxiv.org/abs/2107.05204 · HTML: https://mlsanity.com/paper/2107.05204/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [COLD: Towards the Next Generation of Pre-Ranking System](https://mlsanity.com/paper/2007.16122/) (2020)
- [Neural Attentive Multiview Machines](https://mlsanity.com/paper/2002.07696/) (2020)
- [Managing Diversity in Airbnb Search](https://mlsanity.com/paper/2004.02621/) (2020)
- [Adaptive, Personalized Diversity for Visual Discovery](https://mlsanity.com/paper/1810.01477/) (2018)

## Built on by (most cited first, 15 of 15)

- [Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation](https://mlsanity.com/paper/2512.01372/) (2025)
- [Dual-Phase Playtime-guided Recommendation: Interest Intensity Exploration and Multimodal Random Walks](https://mlsanity.com/paper/2508.14058/) (2025)
- [A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search](https://mlsanity.com/paper/2405.15521/) (2024)
- [Sequential Recommendation with Controllable Diversification: Representation Degeneration and Diversity](https://mlsanity.com/paper/2306.11986/) (2023)
- [Better Late Than Never: Formulating and Benchmarking Recommendation Editing](https://mlsanity.com/paper/2406.04553/) (2024)
- [A Metric for MLLM Alignment in Large-scale Recommendation](https://mlsanity.com/paper/2508.04963/) (2025)
- [Intra-session Context-aware Feed Recommendation in Live Systems](https://mlsanity.com/paper/2210.07815/) (2022)
- [Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender](https://mlsanity.com/paper/2306.00009/) (2023)
- [EnhancedRL: An Enhanced-State Reinforcement Learning Algorithm for Multi-Task Fusion in Recommender Systems](https://mlsanity.com/paper/2409.11678/) (2024)
- [Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System](https://mlsanity.com/paper/2506.00828/) (2025)
- [Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models](https://mlsanity.com/paper/2508.04032/) (2025)
- [SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system](https://mlsanity.com/paper/2508.09090/) (2025)
- [Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure](https://mlsanity.com/paper/2512.17733/) (2025)
- [PaletteID: Prototype-Composed Semantic Identifiers for Multimodal CTR Prediction](https://mlsanity.com/paper/2607.29000/) (2026)
- [LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era](https://mlsanity.com/paper/2609.18148/) (2026)

## Abstract (arXiv)

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.