论文标题
在线交互平台中的稀疏感知神经用户行为建模
Sparsity-aware neural user behavior modeling in online interaction platforms
论文作者
论文摘要
现代在线平台为用户提供了参与各种内容创建，社交网络和购物活动的机会。随着此类在线服务的快速扩散，学习数据驱动的用户行为模型是必不可少的，以实现个性化的用户体验。最近，代表性学习已成为用户建模的有效策略，该策略由在大量交互数据中训练的神经网络提供支持。尽管它们具有巨大的潜力，但我们遇到了绝大多数实体的数据稀疏性的独特挑战，例如，在实体和实体级别的相互作用（冷启动的用户，长尾巴中的物品和短暂群体的项目）中的地面标签中的稀疏性。 在本文中，我们为用户行为建模开发了可推广的神经表示学习框架，旨在应对跨应用程序的不同稀疏挑战。我们的问题设置涵盖了转导和归纳学习方案，在训练和归纳学习目标中看到的the导模型实体仅在推理过程中观察到。我们利用反映用户行为的不同方面（例如，社交网络中的互连性，时间和归因的交互信息）来启用个性化的推论。我们提出的模型是神经体系结构选择的同时进步的补充，并且适应在线平台中快速添加新应用程序。
Modern online platforms offer users an opportunity to participate in a variety of content-creation, social networking, and shopping activities. With the rapid proliferation of such online services, learning data-driven user behavior models is indispensable to enable personalized user experiences. Recently, representation learning has emerged as an effective strategy for user modeling, powered by neural networks trained over large volumes of interaction data. Despite their enormous potential, we encounter the unique challenge of data sparsity for a vast majority of entities, e.g., sparsity in ground-truth labels for entities and in entity-level interactions (cold-start users, items in the long-tail, and ephemeral groups). In this dissertation, we develop generalizable neural representation learning frameworks for user behavior modeling designed to address different sparsity challenges across applications. Our problem settings span transductive and inductive learning scenarios, where transductive learning models entities seen during training and inductive learning targets entities that are only observed during inference. We leverage different facets of information reflecting user behavior (e.g., interconnectivity in social networks, temporal and attributed interaction information) to enable personalized inference at scale. Our proposed models are complementary to concurrent advances in neural architectural choices and are adaptive to the rapid addition of new applications in online platforms.
