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。

基于知识图谱的语义对比学习改进内容推荐：多样性和冷启动用户

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

In this paper, at first, the impact of ImageNet pre-training on fine-grained
Facial Emotion Recognition (FER) is investigated which shows that when enough
augmentations on images are applied, training from scratch provides better
result than fine-tuning on ImageNet pre-training. Next, we propose a method to
improve fine-grained and in-the-wild FER, called Hybrid Multi-Task Learning
(HMTL). HMTL uses Self-Supervised Learning (SSL) as an auxiliary task during
classical Supervised Learning (SL) in the form of Multi-Task Learning (MTL).
Leveraging SSL during training can gain additional information from images for
the primary fine-grained SL task. We investigate how proposed HMTL can be used
in the FER domain by designing two customized version of common pre-text task
techniques, puzzling and in-painting. We achieve state-of-the-art results on
the AffectNet benchmark via two types of HMTL, without utilizing pre-training
on additional data. Experimental results on the common SSL pre-training and
proposed HMTL demonstrate the difference and superiority of our work. However,
HMTL is not only limited to FER domain. Experiments on two types of
fine-grained facial tasks, i.e., head pose estimation and gender recognition,
reveals the potential of using HMTL to improve fine-grained facial
representation.

本文探讨了 ImageNet 预训练对细粒度面部情感识别的影响，并提出了一种名为混合多任务学习（HMTL）的方法来改善细粒度情感识别，使用自我监督学习（SSL）作为辅助任务来获得额外的信息，并通过两种类型的 HMTL 在 AffectNet 基准上取得了最新的成果，此外还讨论了在细粒度面部任务中采用 HMTL 来改进细粒度面部表示的潜力。