Statistical Relational Learning Towards Modelling Social Media Users / 4365
Golnoosh Farnadi
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Nowadays web users actively generate content on different social media platforms. The large number of users requiring personalized services creates a unique opportunity for researchers to explore user modelling. Substantial research has been done by utilizing user generated content to model users by applying different classification or regression techniques. These techniques are powerful types of machine learning approaches, however they only partially model social media users. In this work, we introduce a new statistical relational learning (SRL) framework suitable for this purpose, which we call PSL^Q. PSL^Q is the first SRL framework that supports reasoning with soft quantifiers, such as “most” and “a few.” Indeed, in models for social media it is common to assume that friends are influenced by each other’s behavior, beliefs, and preferences. Thus, having a trait only becomes probable once most or some of one’s friends have that trait. Expressing this dependency requires a soft quantifier, which can be modeled with PSL^Q. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results.