We study the problem of recommending relevant products to users in relatively resource-
scarce markets by leveraging data from similar, richer in resource auxiliary markets. We …

Recommender systems often face heterogeneous datasets containing highly personalized
historical data of users, where no single model could give the best recommendation for …

As more and more novel algorithms being proposed, it is difficult for engineers to choose
suitable algorithms to solve engineering problems in manufacturing systems. According to …

The cold-start problem has always been a key challenge in the recommendation research
field. As a popular method to learn a learner that can rapidly adapt to a new task through a …

While other areas of machine learning have seen more and more automation, designing a
high-performing recommender system still requires a high level of human effort …

We introduce the first 'living lab'for scholarly recommender systems. This lab allows
recommender-system researchers to conduct online evaluations of their novel algorithms for …

Purpose This paper aims to describe the use of a meta-learning framework for
recommending cost-sensitive classification methods with the aim of answering an important …

Recommendation algorithms perform differently if the users, recommendation contexts,
applications, and user interfaces vary even slightly. It is similarly observed in other fields …

Abstract “Federated Meta-Learning”(FML), a concept that allows everyone to benefit from the
data that is generated through software libraries including machine learning and data …

推荐系统作为缓解“信息过载” 的工具, 为用户过滤冗余信息并提供个性化服务,
近年来得到了广泛应用. 然而, 实际推荐场景中, 通常存在冷启动与不同推荐算法难以根据实际 …