A Hybrid Bandit Framework for Diversified Recommendation
This work tackles the problem of incorporating diversity preferences into interactive recommender systems, which is important for users who desire varied recommendations, but the abstract does not provide specific numbers or strong claims of broad impact, suggesting an incremental contribution to the field.
This paper addresses the problem of diversified recommendation in interactive recommender systems by proposing the Linear Modular Dispersion Bandit (LMDB) framework. LMDB optimizes a combination of modular functions for item relevance and dispersion functions for set diversity, demonstrating effectiveness in balancing accuracy and diversity on real datasets.
The interactive recommender systems involve users in the recommendation procedure by receiving timely user feedback to update the recommendation policy. Therefore, they are widely used in real application scenarios. Previous interactive recommendation methods primarily focus on learning users' personalized preferences on the relevance properties of an item set. However, the investigation of users' personalized preferences on the diversity properties of an item set is usually ignored. To overcome this problem, we propose the Linear Modular Dispersion Bandit (LMDB) framework, which is an online learning setting for optimizing a combination of modular functions and dispersion functions. Specifically, LMDB employs modular functions to model the relevance properties of each item, and dispersion functions to describe the diversity properties of an item set. Moreover, we also develop a learning algorithm, called Linear Modular Dispersion Hybrid (LMDH) to solve the LMDB problem and derive a gap-free bound on its n-step regret. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed LMDB framework in balancing the recommendation accuracy and diversity.