Token-Controlled Re-ranking for Sequential Recommendation via LLMs

TL;DR

This paper introduces COREC, a token-controlled re-ranking framework using LLMs that allows users to actively steer recommendations with attribute-based signals, improving personalization and adherence to user constraints.

Contribution

The paper presents a novel token-augmented re-ranking method enabling fine-grained user control in LLM-based recommender systems, balancing preferences and constraints.

Findings

Outperforms state-of-the-art baselines in recommendation effectiveness

Demonstrates superior adherence to attribute-specific user requirements

Enables predictable and flexible manipulation of rankings

Abstract

The widespread adoption of Large Language Models (LLMs) as re-rankers is shifting recommender systems towards a user-centric paradigm. However, a significant gap remains: current re-rankers often lack mechanisms for fine-grained user control. They struggle to balance inherent user preferences with multiple attribute-based constraints, often resorting to simplistic hard filtering that can excessively narrow the recommendation pool and yield suboptimal results. This limitation leaves users as passive recipients rather than active collaborators in the recommendation process. To bridge this gap, we propose COREC, a novel token-augmented re-ranking framework that incorporates specific user requirements in co-creating the recommendation outcome. COREC empowers users to steer re-ranking results with precise and flexible control via explicit, attribute-based signals. The framework learns to balance these commands against latent preferences, yielding rankings that adhere to user instructions without sacrificing personalization. Experiments show that COREC: (1) exceeds state-of-the-art baselines on standard recommendation effectiveness and (2) demonstrates superior adherence to specific attribute requirements, proving that COREC enables fine-grained and predictable manipulation of the rankings.