The data sets available to train models for in silico drug discovery efforts are often small.
Indeed, the sparse availability of labeled data is a major barrier to artificial-intelligence …

Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

首先根据处理形式的不同, 介绍了不同形式数据的特征和各自的典型应用场景以及相应的代表性
处理系统, 总结了大数据处理系统的三大发展趋势; 随后, 对系统支撑下的大数据分析技术和应用 …

Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

To address the long-standing data sparsity problem in recommender systems (RSs), cross-
domain recommendation (CDR) has been proposed to leverage the relatively richer …

Federated learning (FL) is quickly becoming the de facto standard for the distributed training
of deep recommendation models, using on-device user data and reducing server costs. In a …

Abstract Machine learning and data mining techniques have been used in numerous real-
world applications. An assumption of traditional machine learning methodologies is the …

Recent online services rely heavily on automatic personalization to recommend relevant
content to a large number of users. This requires systems to scale promptly to accommodate …

Data sparsity is one of the most challenging problems for recommender systems. One
promising solution to this problem is cross-domain recommendation, ie, leveraging …

Over the past two decades, a large amount of research effort has been devoted to
developing algorithms that generate recommendations. The resulting research progress has …