Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users

arXiv:2403.18667v1 Announce Type: cross
Abstract: Addressing the challenges related to data sparsity, cold-start problems, and diversity in recommendation systems is both crucial and demanding. Many current solutions leverage knowledge graphs to tackle these issues by combining both item-based and user-item collaborative signals. A common trend in these approaches focuses on improving ranking performance at the cost of escalating model complexity, reducing diversity, and complicating the task. It is essential to provide recommendations that are both personalized and diverse, rather than …

abstract arxiv challenges collaborative cs.cl cs.ir current data diversity graph graph-based graphs improving knowledge knowledge graph knowledge graphs recommendation recommendation systems semantic solutions sparsity systems trend type