With information systems becoming larger scale, recommendation systems are a topic of
growing interest in machine learning research and industry. Even though progress on …

News platforms exhibit both the challenges as well as opportunities for enhancing the
functionalities of recommendation systems in today's big data environment. Novel use of big …

In the'Big Data'era, many real-world applications like search involve the ranking problem for
a large number of items. It is important to obtain effective ranking results and at the same …

In Recommender Systems, impressions are a relatively new type of information that records
all products previously shown to the users. They are also a complex source of information …

Key problems arising in web applications (with millions of users and thousands of items) can
be formulated as linear programs involving billions to trillions of decision variables and …

Recommender system fairness has been studied from the perspectives of a variety of
stakeholders including content producers, the content itself and recipients of …

Operationalizing AI fairness at LinkedIn's scale is challenging not only because there are
multiple mutually incompatible definitions of fairness but also because determining what is …

Social networks and content publishing platforms have newsfeed applications, which show
both organic content to drive engagement, and ads to drive revenue. This paper focuses on …

LinkedIn dynamically delivers update activities from a user's interpersonal network to more
than 300 million members in the personalized feed that ranks activities according their" …

The social web has brought about many new types of recommender systems. One of the
most important is recommendation of people, which bears many unique characteristics and …