  Modern techniques in Content-based Recommendation (CBR) leverage item content
information to provide personalized services to users, but suffer from
resource-intensive training on large datasets. To address this issue, we
explore the dataset condensation for textual CBR in this paper. The goal of
dataset condensation is to synthesize a small yet informative dataset, upon
which models can achieve performance comparable to those trained on large
datasets. While existing condensation approaches are tailored to classification
tasks for continuous data like images or embeddings, direct application of them
to CBR has limitations. To bridge this gap, we investigate efficient dataset
condensation for content-based recommendation. Inspired by the remarkable
abilities of large language models (LLMs) in text comprehension and generation,
we leverage LLMs to empower the generation of textual content during
condensation. To handle the interaction data involving both users and items, we
devise a dual-level condensation method: content-level and user-level. At
content-level, we utilize LLMs to condense all contents of an item into a new
informative title. At user-level, we design a clustering-based synthesis
module, where we first utilize LLMs to extract user interests. Then, the user
interests and user embeddings are incorporated to condense users and generate
interactions for condensed users. Notably, the condensation paradigm of this
method is forward and free from iterative optimization on the synthesized
dataset. Extensive empirical findings from our study, conducted on three
authentic datasets, substantiate the efficacy of the proposed method.
Particularly, we are able to approximate up to 97% of the original performance
while reducing the dataset size by 95% (i.e., on dataset MIND).
