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

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 …

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 …

Numerical predictive modeling is widely used in different application domains. Although
many modeling techniques have been proposed, and a number of different aggregate …

We present CaMeLS, a proof of concept of a cooperative meta-learning service for
recommender systems. CaMeLS leverages the computing power of recommender systems …

Bibliographic reference parsers extract machine-readable metadata such as author names,
title, journal, and year from bibliographic reference strings. To extract the metadata, the …

The difficulty in finding the most suitable recommendation algorithm for all requests is a
common challenge in the recommendation system context, regardless of the domain …

We consider algorithm selection in the context of ad-hoc information retrieval. Given a query
and a pair of retrieval methods, we propose a meta-learner that predicts how to combine the …

Online marketplaces must optimize recommendations with regards to multiple objectives, in
order to fulfill expectations from a variety of stakeholders. This problem is typically …