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.

中文翻译：

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改进内容推荐：针对多样性和冷启动用户的基于知识图谱的语义对比学习

解决推荐系统中与数据稀疏、冷启动问题和多样性相关的挑战既至关重要又要求很高。当前的许多解决方案利用知识图通过结合基于项目和用户项目协作信号来解决这些问题。这些方法的一个共同趋势侧重于提高排名性能，但代价是增加模型复杂性、减少多样性和使任务复杂化。提供个性化和多样化的推荐是至关重要的，而不是仅仅依靠实现基于排名的高绩效，例如点击率、召回率等。在本文中，我们提出了一种混合多任务学习方法，关于用户-项目和项目-项目交互的培训。我们对描述性文本应用基于项目的对比学习，根据项目元数据对正负对进行采样。我们的方法允许模型通过利用文本中的语义信息更好地理解知识图中实体之间的关系。它可以带来更准确、相关和多样化的用户推荐，甚至可以惠及与项目交互很少的冷启动用户。我们对两个广泛使用的数据集进行了广泛的实验，以验证我们方法的有效性。我们的研究结果表明，使用概要文本联合训练用户-项目交互和基于项目的信号是非常有效的。此外，我们的结果提供了证据，表明基于项目的对比学习提高了实体嵌入的质量，如均匀性和对齐等指标所示。