  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 solely relying on achieving high rank-based performance, such as
Click-through Rate, Recall, etc. In this paper, we propose a hybrid multi-task
learning approach, training on user-item and item-item interactions. We apply
item-based contrastive learning on descriptive text, sampling positive and
negative pairs based on item metadata. Our approach allows the model to better
understand the relationships between entities within the knowledge graph by
utilizing semantic information from text. It leads to more accurate, relevant,
and diverse user recommendations and a benefit that extends even to cold-start
users who have few interactions with items. We perform extensive experiments on
two widely used datasets to validate the effectiveness of our approach. Our
findings demonstrate that jointly training user-item interactions and
item-based signals using synopsis text is highly effective. Furthermore, our
results provide evidence that item-based contrastive learning enhances the
quality of entity embeddings, as indicated by metrics such as uniformity and
alignment.
