Social Filtering: User-Centric Approach to Social Trend Prediction

Abstract

The majority of techniques in socio-behavioral modeling tend to consider user-generated content in a bulk, with the assumption that this sort of aggregation would not have any negative impact on overall predictability of the system, which is not necessarily the case. We propose a novel user-centric approach designed specifically to capture most predictive hidden variables that can be discovered in a context of the specific individual. The concept of social filtering closely resembles collaborative filtering with the main difference that none of the considered users intentionally participates in the recommendation process. Its objective is to determine both the subset of best expert users able to reflect a particular social trend of interest and their transformation into feature space used for modeling. We introduce three-step selection procedure that includes activity-and relevance-based filtering and ensemble of expert users, and show that proper choice of expert individuals is critical to prediction quality.