Partial Bandit and Semi-Bandit: Making the Most Out of Scarce Users' Feedback

TL;DR

This paper introduces a novel combinatorial bandit approach that reduces the need for explicit user feedback in recommender systems, achieving comparable accuracy with significantly less user input.

Contribution

The paper proposes a new method for incorporating user feedback in COM-MAB algorithms that minimizes explicit feedback requirements while maintaining high accuracy.

Findings

Achieves similar accuracy with only 20% user feedback

Reduces explicit feedback needs in recommender systems

Maintains learning efficiency comparable to state-of-the-art methods

Abstract

Recent works on Multi-Armed Bandits (MAB) and Combinatorial Multi-Armed Bandits (COM-MAB) show good results on a global accuracy metric. This can be achieved, in the case of recommender systems, with personalization. However, with a combinatorial online learning approach, personalization implies a large amount of user feedbacks. Such feedbacks can be hard to acquire when users need to be directly and frequently solicited. For a number of fields of activities undergoing the digitization of their business, online learning is unavoidable. Thus, a number of approaches allowing implicit user feedback retrieval have been implemented. Nevertheless, this implicit feedback can be misleading or inefficient for the agent's learning. Herein, we propose a novel approach reducing the number of explicit feedbacks required by Combinatorial Multi Armed bandit (COM-MAB) algorithms while providing similar levels of global accuracy and learning efficiency to classical competitive methods. In this paper we present a novel approach for considering user feedback and evaluate it using three distinct strategies. Despite a limited number of feedbacks returned by users (as low as 20% of the total), our approach obtains similar results to those of state of the art approaches.