  Large Language Models (LLMs) have recently shown impressive abilities in
handling various natural language-related tasks. Among different LLMs, current
studies have assessed ChatGPT's superior performance across manifold tasks,
especially under the zero/few-shot prompting conditions. Given such successes,
the Recommender Systems (RSs) research community have started investigating its
potential applications within the recommendation scenario. However, although
various methods have been proposed to integrate ChatGPT's capabilities into
RSs, current research struggles to comprehensively evaluate such models while
considering the peculiarities of generative models. Often, evaluations do not
consider hallucinations, duplications, and out-of-the-closed domain
recommendations and solely focus on accuracy metrics, neglecting the impact on
beyond-accuracy facets. To bridge this gap, we propose a robust evaluation
pipeline to assess ChatGPT's ability as an RS and post-process ChatGPT
recommendations to account for these aspects. Through this pipeline, we
investigate ChatGPT-3.5 and ChatGPT-4 performance in the recommendation task
under the zero-shot condition employing the role-playing prompt. We analyze the
model's functionality in three settings: the Top-N Recommendation, the
cold-start recommendation, and the re-ranking of a list of recommendations, and
in three domains: movies, music, and books. The experiments reveal that ChatGPT
exhibits higher accuracy than the baselines on books domain. It also excels in
re-ranking and cold-start scenarios while maintaining reasonable
beyond-accuracy metrics. Furthermore, we measure the similarity between the
ChatGPT recommendations and the other recommenders, providing insights about
how ChatGPT could be categorized in the realm of recommender systems. The
evaluation pipeline is publicly released for future research.
