The Use of Bandit Algorithms in Intelligent Interactive Recommender Systems

TL;DR

This paper explores the application of multi-armed bandit algorithms in intelligent interactive recommender systems, emphasizing their potential for personalized, adaptive suggestions but noting current limitations in handling modern system changes.

Contribution

It analyzes the use of bandit algorithms in recommender systems and discusses challenges in adapting these models to evolving online environments.

Findings

Bandit algorithms effectively personalize recommendations.

Current models struggle with adapting to system changes.

Potential improvements for adaptive bandit models are identified.

Abstract

In today's business marketplace, many high-tech Internet enterprises constantly explore innovative ways to provide optimal online user experiences for gaining competitive advantages. The great needs of developing intelligent interactive recommendation systems are indicated, which could sequentially suggest users the most proper items by accurately predicting their preferences, while receiving the up-to-date feedback to refine the recommendation results, continuously. Multi-armed bandit algorithms, which have been widely applied into various online systems, are quite capable of delivering such efficient recommendation services. However, few existing bandit models are able to adapt to new changes introduced by the modern recommender systems.