Scalable structured prediction for richly structured socio-behavioral data

Abstract

Online recommender systems, content-provider sites, and social media platforms provide richly structured socio-behavioral data. However, using this noisy and incomplete data to make decisions and recommendations is challenging. It often requires complex forms of structured prediction that rely on both the logical structure in the domain and probabilistic dependencies among interlinked entities. In this talk, I will describe some common inference patterns that are useful for socio-behavioral networks and introduce probabilistic soft logic (PSL). PSL is a highly scalable open-source probabilistic programming language being developed within my group that is well-suited for structured prediction over socio-behavioral data. Finally, I will review some of our recent work using PSL for hybrid recommender systems, explanation, and fair decision making.