[2602.11062] MoToRec: Sparse-Regularized Multimodal Tokenization for Cold-Start Recommendation

Computer Science > Machine Learning arXiv:2602.11062 (cs) [Submitted on 11 Feb 2026 (v1), last revised 3 Mar 2026 (this version, v2)] Title:MoToRec: Sparse-Regularized Multimodal Tokenization for Cold-Start Recommendation Authors:Jialin Liu, Zhaorui Zhang, Ray C.C. Cheung View a PDF of the paper titled MoToRec: Sparse-Regularized Multimodal Tokenization for Cold-Start Recommendation, by Jialin Liu and 2 other authors View PDF HTML (experimental) Abstract:Graph neural networks (GNNs) have revolutionized recommender systems by effectively modeling complex user-item interactions, yet data sparsity and the item cold-start problem significantly impair performance, particularly for new items with limited or no interaction history. While multimodal content offers a promising solution, existing methods result in suboptimal representations for new items due to noise and entanglement in sparse data. To address this, we transform multimodal recommendation into discrete semantic tokenization. We present Sparse-Regularized Multimodal Tokenization for Cold-Start Recommendation (MoToRec), a framework centered on a sparsely-regularized Residual Quantized Variational Autoencoder (RQ-VAE) that generates a compositional semantic code of discrete, interpretable tokens, promoting disentangled representations. MoToRec's architecture is enhanced by three synergistic components: (1) a sparsely-regularized RQ-VAE that promotes disentangled representations, (2) a novel adaptive rarity amplification...