Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders
This work provides a systematic overview and practical guidance for researchers and practitioners in the field of LLM-powered recommender systems, but it is incremental as it organizes existing approaches rather than introducing new methods.
The paper tackles the problem of classifying and comparing LLM-based recommender systems by proposing a taxonomy that divides them into pure and augmented categories, and introduces a unified evaluation platform for benchmarking under consistent settings.
Large language models (LLMs) have introduced new paradigms for recommender systems by enabling richer semantic understanding and incorporating implicit world knowledge. In this study, we propose a systematic taxonomy that classifies existing approaches into two categories: (1) Pure LLM Recommenders, which rely solely on LLMs, and (2) Augmented LLM Recommenders, which integrate additional non-LLM techniques to enhance performance. This taxonomy provides a novel lens through which to examine the evolving landscape of LLM-based recommendation. To support fair comparison, we introduce a unified evaluation platform that benchmarks representative models under consistent experimental settings, highlighting key design choices that impact effectiveness. We conclude by discussing open challenges and outlining promising directions for future research. This work offers both a comprehensive overview and practical guidance for advancing next-generation LLM-powered recommender.