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Recommending novel content, which expands user horizons by introducing themto new interests, has been shown to improve users' long-term experience onrecommendation platforms \cite{chen2021values}. Users however are notconstantly looking to explore novel content. It is therefore crucial tounderstand their novelty-seeking intent and adjust the recommendation policyaccordingly. Most existing literature models a user's propensity to choosenovel content or to prefer a more diverse set of recommendations at individualinteractions. Hierarchical structure, on the other hand, exists in a user'snovelty-seeking intent, which is manifested as a static and intrinsic userpreference for seeking novelty along with a dynamic session-based propensity.To this end, we propose a novel hierarchical reinforcement learning-basedmethod to model the hierarchical user novelty-seeking intent, and to adapt therecommendation policy accordingly based on the extracted user novelty-seekingpropensity. We further incorporate diversity and novelty-related measurement inthe reward function of the hierarchical RL (HRL) agent to encourage userexploration \cite{chen2021values}. We demonstrate the benefits of explicitlymodeling hierarchical user novelty-seeking intent in recommendations throughextensive experiments on simulated and real-world datasets. In particular, wedemonstrate that the effectiveness of our proposed hierarchical RL-based methodlies in its ability to capture such hierarchically-structured intent. As aresult, the proposed HRL model achieves superior performance on several publicdatasets, compared with state-of-art baselines.

Hierarchical Reinforcement Learning for Modeling User Novelty-Seeking Intent in Recommender Systems