# Streaming Recommender Systems

arXiv: https://arxiv.org/abs/1607.06182 · HTML: https://mlsanity.com/paper/1607.06182/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2016.

## Built on by (most cited first, 17 of 17)

- [Causal Incremental Graph Convolution for Recommender System Retraining](https://mlsanity.com/paper/2108.06889/) (2021)
- [HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation](https://mlsanity.com/paper/2308.08497/) (2023)
- [Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System](https://mlsanity.com/paper/2308.07760/) (2023)
- [GPT4Rec: Graph Prompt Tuning for Streaming Recommendation](https://mlsanity.com/paper/2406.08229/) (2024)
- [Instant Representation Learning for Recommendation over Large Dynamic Graphs](https://mlsanity.com/paper/2305.18622/) (2023)
- [Exploring Backdoor Attack and Defense for LLM-empowered Recommendations](https://mlsanity.com/paper/2504.11182/) (2025)
- [Lightweight Compositional Embeddings for Incremental Streaming Recommendation](https://mlsanity.com/paper/2202.02427/) (2022)
- [InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Update](https://mlsanity.com/paper/2309.11071/) (2023)
- [IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History](https://mlsanity.com/paper/2403.16075/) (2024)
- [Enhancing Bandit Algorithms with LLMs for Time-varying User Preferences in Streaming Recommendations](https://mlsanity.com/paper/2602.08067/) (2026)
- [Momentum LMS Theory beyond Stationarity: Stability, Tracking, and Regret](https://mlsanity.com/paper/2602.11995/) (2026)
- [Online Interaction Detection for Click-Through Rate Prediction](https://mlsanity.com/paper/2106.15400/) (2021)
- [Leaping Through Time with Gradient-based Adaptation for Recommendation](https://mlsanity.com/paper/2112.05914/) (2021)
- [DiPS: Differentiable Policy for Sketching in Recommender Systems](https://mlsanity.com/paper/2112.07616/) (2021)
- [Fast Second-Order Online Kernel Learning through Incremental Matrix Sketching and Decomposition](https://mlsanity.com/paper/2410.11188/) (2024)
- [Feature Staleness Aware Incremental Learning for CTR Prediction](https://mlsanity.com/paper/2505.02844/) (2025)
- [Model-agnostic post-hoc explainability for recommender systems](https://mlsanity.com/paper/2509.10245/) (2025)

## Abstract (arXiv)

The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios. Data streams present distinct properties such as temporally ordered, continuous and high-velocity, which poses tremendous challenges to traditional recommender systems. In this paper, we investigate the problem of recommendation with stream inputs. In particular, we provide a principled framework termed sRec, which provides explicit continuous-time random process models of the creation of users and topics, and of the evolution of their interests. A variational Bayesian approach called recursive meanfield approximation is proposed, which permits computationally efficient instantaneous on-line inference. Experimental results on several real-world datasets demonstrate the advantages of our sRec over other state-of-the-arts.