Predicting trusts among users of online communities

A taxonomy is developed to obtain an extensive set of relevant features derived from user attributes and user interactions in an online community and empirical results show that the trust among users can be effectively predicted using pre-trained classifiers.

Abstract

Embedding deals with reducing the high-dimensional representation of data into a low-dimensional representation. Previous work mostly focuses on preserving similarities among objects. Here, not only do we explicitly recognize multiple types of objects, but we also focus on the ordinal relationships across types. Collaborative Ordinal Embedding or COE is based on generative modelling of ordinal triples. Experiments show that COE outperforms the baselines on objective metrics, revealing its capacity for information preservation for ordinal data.