In recent years it has become popular to study machine learning problems based on ordinal distance information rather than numerical distance measurements. By ordinal distance information we refer to binary answers to distance comparisons such as d(A,B) < d(C,D). For many problems in machine learning and statistics it is unclear how to solve them in such a scenario. Up to now, the main approach...

Recommender Systems heavily rely on numerical preferences, whereas the importance of ordinal preferences has only been recognised in recent works of Ordinal Matrix Factorisation (OMF). Although the OMF can effectively exploit ordinal properties, it captures only the higher-order interactions among users and items, without considering the localised interactions properly. This paper employs Marko...

Ordinal sums have been introduced in many different contexts, e.g., for posets, semigroups, t-norms, copulas, aggregation operators, or quite recently for hoops. In this contribution, we focus on ordinal sums of t-norms acting on some bounded lattice which is not necessarily a chain or an ordinal sum of posets. Necessary and sufficient conditions are provided for an ordinal sum operation yieldi...

Social scientists, particularly political scientists, frequently use ordinal survey items as dependent variables in models of political attitudes. Commonly, normal-theory modeling strategies like ordinary least squares regression are applied to these items. Additionally, workers also make frequent use of the proportional odds (ordinal logit) model or cumulative probit model when working with su...

We describe a simple model of ordinal computation which can compute truth in the constructible universe. We try to use well-structured programs and direct limits of states at limit times whenever possible. This may make it easier to define a model of ordinal computation within other systems of hypercomputation, especially systems inspired by physical models. We write a program to compute truth ...