The goal of this tutorial is to expose participants to the current research on social
recommender systems (ie, recommender systems for the social web). Participants will …

Stochastic gradient descent (SGD) is a ubiquitous algorithm for a variety of machine learning
problems. Researchers and industry have developed several techniques to optimize SGD's …

The effects of online social media on critical issues, such as polarization and misinformation,
are under scrutiny due to the disruptive consequences that these phenomena can have on …

User recommender systems are a key component in any on-line social networking platform:
they help the users growing their network faster, thus driving engagement and loyalty. In this …

For large-scale graph analytics on the GPU, the irregularity of data access and control flow,
and the complexity of programming GPUs, have presented two significant challenges to …

Many recommenders aim to provide relevant recommendations to users by building
personal topic interest profiles and then using these profiles to find interesting contents for …

Stochastic gradient descent (SGD) on a low-rank factorization is commonly employed to
speed up matrix problems including matrix completion, subspace tracking, and SDP …

We present new algorithms for Personalized PageRank estimation and Personalized
PageRank search. First, for the problem of estimating Personalized PageRank (PPR) from a …

Related Pins is the Web-scale recommender system that powers over 40% of user
engagement on Pinterest. This paper is a longitudinal study of three years of its …

Link prediction, a fundamental task on graphs, has proven indispensable in various
applications, eg, friend recommendation, protein analysis, and drug interaction prediction …