Enhancing Collective Filtering with Causal Representation

In this paper, we propose to enhance the practice of web-based collective filtering with the addition of a causality linking module. Causality lies at the foundations of human understanding, when presented in visual form, is especially suited to the task as it is intuitive to understand and to use. But in its simplicity, causality could provide a semantic network over the filtering tool, connecting representations of real world facts