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 …

As one of the most successful approaches to building recommender systems, collaborative
filtering (CF) uses the known preferences of a group of users to make recommendations or …

全面地总结推荐系统的研究现状, 旨在介绍网络推荐的算法思想, 帮助读者了解这个研究领域.
首先阐述了推荐系统研究的工业需求, 主要研究机构和成果发表的期刊会议; …

对现有的推荐系统评价指标进行了系统的回顾, 总结了推荐系统评价指标的最新研究进展,
从准确度, 多样性, 新颖性及覆盖率等方面进行多角度阐述, 并对各自的优缺点以及适用环境进行 …

An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …

Recommender systems usually make personalized recommendation with user-item
interaction ratings, implicit feedback and auxiliary information. Matrix factorization is the …

The web contains a huge volume of data, and it's populating every moment to the point that
human beings cannot deal with the vast amount of data manually or via traditional tools …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

Although recommender systems (RSs) play a crucial role in our society, previous studies
have revealed that the performance of RSs may considerably differ between groups of …

Modern recommender systems usually employ collaborative filtering with rating information
to recommend items to users due to its successful performance. However, because of the …