Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation

TL;DR

This paper introduces HRL4PFG, a hierarchical reinforcement learning framework that proactively guides user preferences towards long-tail items in interactive recommender systems, balancing fairness and user satisfaction.

Contribution

It proposes a novel proactive fairness-guiding strategy using hierarchical reinforcement learning to improve long-term fairness and engagement in interactive recommendations.

Findings

HRL4PFG outperforms state-of-the-art methods in cumulative rewards.

It significantly increases maximum user interaction length.

The approach effectively balances fairness and user satisfaction.

Abstract

Item-side fairness is crucial for ensuring the fair exposure of long-tail items in interactive recommender systems. Existing approaches promote the exposure of long-tail items by directly incorporating them into recommended results. This causes misalignment between user preferences and the recommended long-tail items, which hinders long-term user engagement and reduces the effectiveness of recommendations. We aim for a proactive fairness-guiding strategy, which actively guides user preferences toward long-tail items while preserving user satisfaction during the interactive recommendation process. To this end, we propose HRL4PFG, an interactive recommendation framework that leverages hierarchical reinforcement learning to guide user preferences toward long-tail items progressively. HRL4PFG operates through a macro-level process that generates fairness-guided targets based on multi-step feedback, and a micro-level process that fine-tunes recommendations in real time according to both these targets and evolving user preferences. Extensive experiments show that HRL4PFG improves cumulative interaction rewards and maximum user interaction length by a larger margin when compared with state-of-the-art methods in interactive recommendation environments.