Aggregation of opinions with a Boltzmann machine

In most research studies, much of the information gathered is qualitative in nature. This article focuses on items for which there are multiple rankings that should be optimally combined. More specifically, it describes a supervised stochastic approach, driven by a Boltzmann machine capable of ranking elements relative to each other in order of importance. Unlike statistical ranking techniques (i) the algorithm does not require any prior knowledge of the data distribution (ii) it is adaptive (iii) it can be used with score or ranking data.