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.

我们提出了一种混合多任务学习方法，通过对描述性文本进行基于物品的对比学习，从而更好地理解知识图谱中的实体关系，提供更准确、相关、多样化的用户推荐，特别对有限物品交互的冷启动用户也能发挥作用。我们在两个广泛使用的数据集上进行了大量实验，验证了我们方法的有效性，并且结果表明基于物品的对比学习提高了实体嵌入的质量。