  Recommender systems are widely used in online services, with embedding-based
models being particularly popular due to their expressiveness in representing
complex signals. However, these models often function as a black box, making
them less transparent and reliable for both users and developers. Recently,
large language models (LLMs) have demonstrated remarkable intelligence in
understanding, reasoning, and instruction following. This paper presents the
initial exploration of using LLMs as surrogate models to explaining black-box
recommender models. The primary concept involves training LLMs to comprehend
and emulate the behavior of target recommender models. By leveraging LLMs' own
extensive world knowledge and multi-step reasoning abilities, these aligned
LLMs can serve as advanced surrogates, capable of reasoning about observations.
Moreover, employing natural language as an interface allows for the creation of
customizable explanations that can be adapted to individual user preferences.
To facilitate an effective alignment, we introduce three methods: behavior
alignment, intention alignment, and hybrid alignment. Behavior alignment
operates in the language space, representing user preferences and item
information as text to mimic the target model's behavior; intention alignment
works in the latent space of the recommendation model, using user and item
representations to understand the model's behavior; hybrid alignment combines
both language and latent spaces. Comprehensive experiments conducted on three
public datasets show that our approach yields promising results in
understanding and mimicking target models, producing high-quality,
high-fidelity, and distinct explanations. Our code is available at
https://github.com/microsoft/RecAI.
