A Cross-Domain Multimodal Supervised Latent Topic Model for Item Tagging and Cold-Start Recommendation

Abstract

Cross-domain data analysis is playing an increasingly important role in media convergence and can be adopted for many applications. Most existing methods consider the domain discrimination as the multimodal representation difference or the imbalanced item classification distribution, which ignore the different tag semantics between domains. To this end, we propose an explainable cross-domain multimodal supervised latent topic model (CDMSLT) and evaluate our model on two applications. First, we learn a common topic space which is capable of explaining both domain specification and commonality. Second, we apply our model to multilabel classification task and put forward a cross-domain item tagging method (CDMSLT-tag). Third, combining user behaviors and CDMSLT model, we propose a cross-domain recommendation algorithm (CDMSLT-rec) which could estimate the user preference on new unseen domains. This paper proves the effectiveness of CDMSLT model by comparing these two applications with existing algorithms in a cross-domain scenario on Douban dataset.