Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback

2026-05-27

AI Summary

Traditional recommender systems heavily rely on implicit signals such as clicks, often neglecting the rich semantic information embedded in explicit contextual feedback like reviews and ratings, which can lead to preference misalignment and filter bubbles. This work is the first to systematically underscore the pivotal role of explicit feedback in large language model (LLM)-driven recommendation and proposes a novel heterogeneous information modeling framework that deeply integrates user-generated textual signals—such as reviews—into a scalable recommendation architecture. The contributions include a new framework, a dedicated evaluation benchmark, and tailored metrics, collectively enhancing recommendation accuracy, diversity, and explainability. This study establishes a new paradigm for next-generation recommender systems that are both transparent and highly personalized.

Abstract

Traditional recommender systems (RecSys) primarily infer user preferences from implicit signals (such as clicks, watches, and purchases), often neglecting the rich explicit contextual feedback users provide through verbal text, like comments and reviews. This explicit context feedback captures the nuanced reasons behind user decisions regarding their preferences. In addition, it offers critical heterogeneous information for user preference alignment and more explainable recommendations. Overlooking such signals can lead to misaligned user preferences and further reinforce filter bubbles, as algorithms fail to understand the "semantic context" behind user choices. Recent advances in Large Language Models (LLMs) present new opportunities to harness user-generated content for more accurate and diverse recommendations, yet current LLM-based recommendations still focus on using item meta-data and underutilize this resource. In this paper, we advocate for prioritizing explicit context feedback in the next generation of LLM-based RecSys. We review the evolution of recommendation paradigms, highlight the value of context-rich feedback, call for new benchmarks and metrics, and introduce frameworks for integrating explicit user signals into scalable LLM-driven RecSys. Centering on user-preference modeling, we aim to foster more personalized, transparent, and explainable RecSys online platforms.

Problem

• explicit context feedback
• user preference alignment
• LLM-based recommendation
• explainable recommender systems
• filter bubbles

Innovation

• explicit context feedback
• LLM-based recommendation
• user preference alignment
• explainable recommender systems
• semantic context