  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.
