Published: 01 Jan 2024, Last Modified: 29 Jul 2025Electron. Commer. Res. Appl. 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract:Highlights•Heterogeneous recommendation is proposed without data sharing.•We focus on cold-start and sparsity in heterogeneous recommendation.•Latent features are extracted for heterogeneous recommendation.