Exploring user-item interaction cues is crucial for the performance of recommender systems.
Explicit investigation of interaction cues is made possible by using graph-based models …

As a key component in online marketing, uplift modeling aims to accurately capture the
degree to which different treatments motivate different users, such as coupons or discounts …

Although knowledge graph has shown their effectiveness in mitigating data sparsity in many
recommendation tasks, they remain underutilized in context-aware recommender systems …

Contextual situations, such as having dinner at a restaurant on Friday with the spouse,
became a useful mechanism to represent context in context-aware recommender systems …

E-commerce search systems such as Taobao Search, the largest e-commerce searching
system in China, aim at providing users with the most preferred items (eg, products). Due to …

Multi-behavior recommender systems (MBRS) have been commonly deployed on real-world
industrial platforms for their superior advantages in understanding user preferences and …

Different from the data sparsity that traditional recommendations suffer from, context-aware
recommender systems (CARS) face specific sparsity challenges related to contextual …

The traditional music appreciation course teaching model relies on questionnaires or
manual decision-making to determine teaching content, which is time-consuming and easily …

Event representation learning is crucial for numerous event-driven tasks, as the quality of
event representations greatly influences the performance of these tasks. However, many …

近年来, 随着众包平台的不断发展, 信息过载问题日趋严重, 任务难以及时找到可靠的开发者完成
, 为任务推荐合适的开发者变得至关重要. 传统推荐方法存在两大挑战: 一是任务和开发者的文本 …