G. Medda

Sparse Autoencoders as Semantic Domain Adapters for Recommender Systems

Pretrained text embeddings enable content-based cold-item recommendation, but their domain-agnostic objectives may underrepresent distinctions that matter within a particular recommendation domain. We investigate lightweight autoencoder adapters that specialize these embeddings without retraining or finetuning the embe...