MMHCL：用于推荐的多模态超图对比学习
MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation
AI总结：
提出MMHCL框架，通过构建用户-用户和商品-商品超图挖掘二阶语义，并结合协同对比学习增强特征区分性，以缓解推荐中的数据稀疏和冷启动问题。
摘要：
多模态内容分享平台的蓬勃发展推动了个性化推荐系统的发展。以往的工作通常面临数据稀疏和冷启动问题，并且可能无法充分从多模态数据中探索语义层面的用户-商品关联。为解决这些问题，我们提出了一种新颖的多模态超图对比学习（MMHCL）框架用于用户推荐。为了从用户-商品关系中全面探索信息，我们构建了两个超图，即用户-用户（u2u）超图和商品-商品（i2i）超图，分别用于挖掘用户间的共享偏好和商品间复杂的多模态语义相似性。这一过程产生了更稠密的二阶语义，并与一阶用户-商品交互相融合，作为补充以缓解数据稀疏问题。然后，我们设计了一种对比特征增强范式，应用协同对比学习。通过最大化/最小化相同/不同用户和商品的二阶（如用户的共享偏好模式）与一阶（用户所选商品的信息）嵌入之间的互信息，可以有效增强特征的可区分性。与仅使用稀疏的一阶用户-商品交互相比，我们的MMHCL获得了更稠密的二阶超图，并挖掘出更丰富的共享属性来探索用户-商品关联，这在一定程度上缓解了数据稀疏和冷启动问题。大量实验全面证明了我们方法的有效性。我们的代码公开于：https://github.com/Xu107/MMHCL。
英文摘要：
The burgeoning presence of multimodal content-sharing platforms propels the development of personalized recommender systems. Previous works usually suffer from data sparsity and cold-start problems, and may fail to adequately explore semantic user-product associations from multimodal data. To address these issues, we propose a novel Multi-Modal Hypergraph Contrastive Learning (MMHCL) framework for user recommendation. For a comprehensive information exploration from user-product relations, we construct two hypergraphs, i.e. a user-to-user (u2u) hypergraph and an item-to-item (i2i) hypergraph, to mine shared preferences among users and intricate multimodal semantic resemblance among items, respectively. This process yields denser second-order semantics that are fused with first-order user-item interaction as complementary to alleviate the data sparsity issue. Then, we design a contrastive feature enhancement paradigm by applying synergistic contrastive learning. By maximizing/minimizing the mutual information between second-order (e.g. shared preference pattern for users) and first-order (information of selected items for users) embeddings of the same/different users and items, the feature distinguishability can be effectively enhanced. Compared with using sparse primary user-item interaction only, our MMHCL obtains denser second-order hypergraphs and excavates more abundant shared attributes to explore the user-product associations, which to a certain extent alleviates the problems of data sparsity and cold-start. Extensive experiments have comprehensively demonstrated the effectiveness of our method. Our code is publicly available at: https://github.com/Xu107/MMHCL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
