# Dynamic User Interest Augmentation via Stream Clustering and Memory Networks in Large-Scale Recommender Systems

arXiv: https://arxiv.org/abs/2405.13238 · HTML: https://mlsanity.com/paper/2405.13238/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 37 references that are in the map.
- First posted on arXiv 21 May 2024.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Finite Scalar Quantization: VQ-VAE Made Simple](https://mlsanity.com/paper/2309.15505/) (2023)
- [Memory Networks](https://mlsanity.com/paper/1410.3916/) (2014)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](https://mlsanity.com/paper/2006.05639/) (2020)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [Learning Tree-based Deep Model for Recommender Systems](https://mlsanity.com/paper/1801.02294/) (2018)
- [Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems](https://mlsanity.com/paper/1902.05570/) (2019)
- [Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://mlsanity.com/paper/1904.11547/) (2019)
- [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://mlsanity.com/paper/2302.01115/) (2023)
- [Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation](https://mlsanity.com/paper/2307.09688/) (2023)
- [A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation](https://mlsanity.com/paper/2010.15982/) (2020)
- [Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks](https://mlsanity.com/paper/2105.04790/) (2021)
- [Monolith: Real Time Recommendation System With Collisionless Embedding Table](https://mlsanity.com/paper/2209.07663/) (2022)
- [Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction](https://mlsanity.com/paper/2205.10249/) (2022)
- [Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems](https://mlsanity.com/paper/2208.04560/) (2022)
- [Unsupervised Paraphrasing without Translation](https://mlsanity.com/paper/1905.12752/) (2019)
- [Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework](https://mlsanity.com/paper/2201.10980/) (2022)
- [POSO: Personalized Cold Start Modules for Large-scale Recommender Systems](https://mlsanity.com/paper/2108.04690/) (2021)
- [Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder](https://mlsanity.com/paper/2205.13795/) (2022)
- [Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems](https://mlsanity.com/paper/2309.15646/) (2023)
- [A comparative study of divisive hierarchical clustering algorithms](https://mlsanity.com/paper/1506.08977/) (2015)

## Built on by (most cited first, 2 of 2)

- [ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Users](https://mlsanity.com/paper/2510.09393/) (2025)
- [SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks](https://mlsanity.com/paper/2606.09888/) (2026)

## Abstract (arXiv)

Recommender System (RS) provides personalized recommendation service based on user interest. However, lots of users' interests are sparse due to lacking consumption behaviors, making it challenging to provide accurate recommendations for them, which is widespread in large-scale RSs. In particular, efficiently solving this problem in the ranking stage of RS is an even greater challenge, which requires an end-to-end and real-time approach. To solve this problem, we propose an innovative method called Dynamic User Interest Augmentation (DUIA). DUIA enhances user interest including user profile and user history behavior sequences by generating enhancement vectors and personalized enhancement vectors through dynamic stream clustering of similar users and relevant items from multiple perspectives. To realize stream clustering, we specially design an algorithm called Gradient-based Hierarchical Clustering Algorithm (GHCA) for DUIA, which performs clustering via gradient descent and stores the cluster centers in memory networks. Extensive offline and online experiments demonstrate that DUIA not only significantly improves model performance for users with sparse interests but also delivers notable gains for other users. As an end-to-end method, DUIA can be easily integrated with existing models. Furthermore, DUIA is also used for long-tail items and cold-start problem, which also yields excellent improvements. Since 2022, DUIA has been successfully deployed in multiple industrial RSs in Tencent and was made public in May 2024. Moreover, the thoughts behind DUIA, dynamic stream clustering and similarity-based enhancement, have inspired relevant works and have also been applied in other stages of RS.