# HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation

arXiv: https://arxiv.org/abs/2308.08497 · HTML: https://mlsanity.com/paper/2308.08497/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 16 of 16)

- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Continual learning with hypernetworks](https://mlsanity.com/paper/1906.00695/) (2019)
- [Personalized Federated Learning using Hypernetworks](https://mlsanity.com/paper/2103.04628/) (2021)
- [Learning the Pareto Front with Hypernetworks](https://mlsanity.com/paper/2010.04104/) (2020)
- [Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions](https://mlsanity.com/paper/1903.03088/) (2019)
- [KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems](https://mlsanity.com/paper/2202.10842/) (2022)
- [Hypernetwork functional image representation](https://mlsanity.com/paper/1902.10404/) (2019)
- [Sequential Batch Learning in Finite-Action Linear Contextual Bandits](https://mlsanity.com/paper/2004.06321/) (2020)
- [A Gang of Bandits](https://mlsanity.com/paper/1306.0811/) (2013)
- [Hyper-Graph-Network Decoders for Block Codes](https://mlsanity.com/paper/1909.09036/) (2019)
- [Contextual Bandits with Cross-learning](https://mlsanity.com/paper/1809.09582/) (2018)
- [Learning Contextual Bandits in a Non-stationary Environment](https://mlsanity.com/paper/1805.09365/) (2018)
- [Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests](https://mlsanity.com/paper/2003.00359/) (2020)
- [Streaming Recommender Systems](https://mlsanity.com/paper/1607.06182/) (2016)
- [Controllable Multi-Objective Re-ranking with Policy Hypernetworks](https://mlsanity.com/paper/2306.05118/) (2023)

## Built on by (most cited first, 10 of 10)

- [A Survey of Controllable Learning: Methods and Applications in Information Retrieval](https://mlsanity.com/paper/2407.06083/) (2024)
- [Interactive Recommendation Agent with Active User Commands](https://mlsanity.com/paper/2509.21317/) (2025)
- [MoRE: A Mixture of Reflectors Framework for Large Language Model-Based Sequential Recommendation](https://mlsanity.com/paper/2409.06377/) (2024)
- [MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation Alignment](https://mlsanity.com/paper/2503.01711/) (2025)
- [On the Decision-Making Abilities in Role-Playing using Large Language Models](https://mlsanity.com/paper/2402.18807/) (2024)
- [Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticity](https://mlsanity.com/paper/2506.12389/) (2025)
- [Enhancing Bandit Algorithms with LLMs for Time-varying User Preferences in Streaming Recommendations](https://mlsanity.com/paper/2602.08067/) (2026)
- [GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items](https://mlsanity.com/paper/2603.14259/) (2026)
- [In-Context Decision Making for Optimizing Complex AutoML Pipelines](https://mlsanity.com/paper/2508.13657/) (2025)
- [Contextual Scalarisation Thompson Sampling for multi-objective decisions in public media](https://mlsanity.com/paper/2605.31291/) (2026)

## Abstract (arXiv)

In real-world streaming recommender systems, user preferences often dynamically change over time (e.g., a user may have different preferences during weekdays and weekends). Existing bandit-based streaming recommendation models only consider time as a timestamp, without explicitly modeling the relationship between time variables and time-varying user preferences. This leads to recommendation models that cannot quickly adapt to dynamic scenarios. To address this issue, we propose a contextual bandit approach using hypernetwork, called HyperBandit, which takes time features as input and dynamically adjusts the recommendation model for time-varying user preferences. Specifically, HyperBandit maintains a neural network capable of generating the parameters for estimating time-varying rewards, taking into account the correlation between time features and user preferences. Using the estimated time-varying rewards, a bandit policy is employed to make online recommendations by learning the latent item contexts. To meet the real-time requirements in streaming recommendation scenarios, we have verified the existence of a low-rank structure in the parameter matrix and utilize low-rank factorization for efficient training. Theoretically, we demonstrate a sublinear regret upper bound against the best policy. Extensive experiments on real-world datasets show that the proposed HyperBandit consistently outperforms the state-of-the-art baselines in terms of accumulated rewards.