Freshness-Aware Thompson Sampling

TL;DR

This paper introduces Freshness-Aware Thompson Sampling (FA-TS), an algorithm for CARS that dynamically balances recommending fresh content based on user context, improving exploration and exploitation strategies.

Contribution

The paper proposes a novel algorithm, FA-TS, that incorporates content freshness and user risk into the bandit framework for improved content recommendation.

Findings

FA-TS effectively manages content freshness based on user risk.

Experimental results show improved exploration/exploitation balance.

The analysis reveals key insights into user interaction dynamics.

Abstract

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.