Freshness-Aware Thompson Sampling
This work addresses the need for dynamic content freshness in recommender systems for users, but it appears incremental as it builds on existing bandit methods.
The paper tackled the problem of recommending fresh content in context-aware recommender systems by modeling it as a bandit problem, proposing the FA-TS algorithm to manage freshness based on user risk, and revealed important discoveries in exploration/exploitation behavior through intensive evaluation.
To follow the dynamicity of the user's content, researchers have recently started to model interactions between users and the Context-Aware Recommender Systems (CARS) as a bandit problem where the system needs to deal with exploration and exploitation dilemma. In this sense, we propose to study the freshness of the user's content in CARS through the bandit problem. We introduce in this paper an algorithm named Freshness-Aware Thompson Sampling (FA-TS) that manages the recommendation of fresh document according to the user's risk of the situation. The intensive evaluation and the detailed analysis of the experimental results reveals several important discoveries in the exploration/exploitation (exr/exp) behaviour.