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.

翻译：针对推荐系统面临的数据稀疏性、冷启动问题和多样性挑战，解决这些难题既关键又具有挑战性。现有方法常借助知识图谱，通过结合基于项目和用户-项目协同信号来应对上述问题。此类方法的主流趋势侧重于提升排序性能，却往往以增加模型复杂度、降低多样性及使任务复杂化为代价。理想的推荐应兼顾个性化与多样性，而非单纯追求点击率、召回率等高排名指标。本文提出一种混合多任务学习方法，对用户-项目与项目-项目交互进行联合训练。在描述性文本上，我们基于项目元数据采样正负样本，执行项目级对比学习。该方法使模型能利用文本语义信息，更深入理解知识图谱中实体间的关联，从而生成更准确、相关且多样化的用户推荐，即便对于交互记录稀少的冷启动用户同样有效。我们在两个广泛使用的数据集上进行了大量实验，验证了方法的有效性。结果表明，利用摘要文本联合训练用户-项目交互与项目级信号具有显著优势。此外，实验结果证实，项目级对比学习可提升实体嵌入质量，体现在均匀性与对齐性等指标上。