Representation and Reasoning with Probabilistic TCP-nets

Abstract

TCP-nets are graphical tools for modeling user's preference and relative importance statements. We propose the Prob-abilistic TCP-net (PTCP-net) model that can aggregate a set of TCP-nets, in a compact form, sharing the same set of variables and their domains but having different preference and relative importance statements. In particular, the PTCP-net is able to aggregate the choices of multiple users such as, in recommender systems. The PTCP-net can also be seen as an extension of the TCP-net with uncertainty on preference and relative importance statements. We adopt the Bayesian Network as the reasoning tool for PTCP-nets especially when answering the following two queries (1) finding the most probable TCP-net and (2) finding the most probable optimal outcome. We also show that the PTCP-net is applicable in collaborative filtering type recommender systems.