RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation

TL;DR

This paper evaluates the RecRanker LLM-based recommendation framework, reproduces its results, identifies data leakage issues, and introduces RecRankerEval, a comprehensive evaluation framework for top-k recommendation models.

Contribution

It provides a reproducibility study of RecRanker, analyzes the impact of its components, and proposes RecRankerEval, an extensible framework for fair evaluation of LLM-based recommendations.

Findings

Reproduction confirms original performance for pairwise and listwise methods.

Data leakage was identified in the pointwise method due to ground-truth prompts.

Performance can be improved with alternative sampling, stronger models, and better LLMs.

Abstract

A recent Large language model (LLM)-based recommendation model, called RecRanker, has demonstrated a superior performance in the top-k recommendation task compared to other models. In particular, RecRanker samples users via clustering, generates an initial ranking list using an initial recommendation model, and fine-tunes an LLM through hybrid instruction tuning to infer user preferences. However, the contribution of each core component remains underexplored. In this work, we inspect the reproducibility of RecRanker, and study the impact and role of its various components. We begin by reproducing the RecRanker pipeline through the implementation of all its key components. Our reproduction shows that the pairwise and listwise methods achieve a performance comparable to that reported in the original paper. For the pointwise method, while we are also able to reproduce the original paper's results, further analysis shows that the performance is abnormally high due to data leakage from the inclusion of ground-truth information in the prompts. To enable a fair and comprehensive evaluation of LLM-based top-k recommendations, we propose RecRankerEval, an extensible framework that covers five key dimensions: user sampling strategy, initial recommendation model, LLM backbone, dataset selection, and instruction tuning method. Using the RecRankerEval framework, we show that the original results of RecRanker can be reproduced on the ML-100K and ML-1M datasets, as well as the additional Amazon-Music dataset, but not on BookCrossing due to the lack of timestamp information in the original RecRanker paper. Furthermore, we demonstrate that RecRanker's performance can be improved by employing alternative user sampling methods, stronger initial recommenders, and more capable LLMs.