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Collaborative filtering based on bi-relational data representation

Szwabe A., Misiorek P, Ciesielczyk M, Jedrzejek C

Foundations of Computing and Decision Sciences

2013

Vol. 38, No. 1

67--83

Widely-referenced approaches to collaborative filtering (CF) are based on the use of an input matrix that represents each user profile as a vector in a space of items and each item as a vector in a space of users. When the behavioral input data have the form of (userX, likes, itemY) and (userX, dislikes, itemY) triples one has to propose a representation of the user feedback data that is more suitable for the use of propositional data than the ordinary user-item ratings matrix. We propose to use an element-fact matrix, in which columns represent RDF-like behavioral data triples and rows represent users, items, and relations. By following such a triple-based approach to the bi-relational behavioral data representation we are able to improve the quality of collaborative filtering. One of the key findings of the research presented in this paper is that the proposed bi-relational behavioral data representation, while combined with reflective matrix processing, significantly outperforms state-of-the-art collaborative filtering methods based on the use of a ‘standard’ user-item matrix.

Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques

Meert W., Struyf J., Blockeel H.

Fundamenta Informaticae

2008

Vol. 89, nr 1

131-160

Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such relations. This paper investigates the learning of CP-logic theories (CP-theories) from training data. Its first contribution is SEM-CP-logic, an algorithm that learns CP-theories by leveraging Bayesian network (BN) learning techniques. SEM-CP-logic is based on a transformation between CP-theories and BNs. That is, the method applies BN learning techniques to learn a CP-theory in the form of an equivalent BN. To this end, certain modifications are required to the BN parameter learning and structure search, the most important one being that the refinement operator used by the search must guarantee that the constructed BNs represent valid CP-theories. The paper’s second contribution is a theoretical and experimental comparison between CP-theory and BN learning. We show that the most simple CP-theories can be represented with BNs consisting of noisy-OR nodes, while more complex theories require close to fully connected networks (unless additional unobserved nodes are introduced in the network). Experiments in a controlled artificial domain show that in the latter cases CP-theory learning with SEM-CP-logic requires fewer training data than BN learning. We also apply SEM-CP-logic in a medical application in the context of HIV research, and show that it can compete with state-of-the-art methods in this domain.