Recommender systems provide personalized information access to users of Internet
services from social networks to e-commerce to media and entertainment. As is appropriate …

With the rapid growth of digital publishing, harvesting, managing, and analyzing scholarly
information have become increasingly challenging. The term Big Scholarly Data is coined …

Recent work on machine learning has begun to consider issues of fairness. In this paper, we
extend the concept of fairness to recommendation. In particular, we show that in some …

Fairness has emerged as an important category of analysis for machine learning systems in
some application areas. In extending the concept of fairness to recommender systems, there …

Abstract Information networks are becoming increasingly popular to capture complex
relationships across various disciplines, such as social networks, citation networks, and …

Scholarly big data, which is a large-scale collection of academic information, technical data,
and collaboration relationships, has attracted increasing attentions, ranging from industries …

Delay tolerant networks (DTNs) may lack continuous network connectivity. Routing in DTNs
is thus challenging since it must handle network partitioning, long delays, and dynamic …

Link prediction in online social networks is used to determine new interactions among its
members which are likely to occur in the future. Link prediction in the co-authorship network …

Recommender systems aim to identify relevant items for particular users in large-scale
online applications. The historical rating data of users is a valuable input resource for many …

Multistakeholder recommendation is the term applied when a recommender system is
designed, implemented and/or evaluated taking into account the perspectives of multiple …