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Open access

Andrzej Szwabe, Pawel Misiorek, Tadeusz Janasiewicz and Przemyslaw Walkowiak

Abstract

We propose a collaborative filtering (CF) method that uses behavioral data provided as propositions having the RDF-compliant form of (user X, likes, item Y ) triples. The method involves the application of a novel self-configuration technique for the generation of vector-space representations optimized from the information-theoretic perspective. The method, referred to as Holistic Probabilistic Modus Ponendo Ponens (HPMPP), enables reasoning about the likelihood of unknown facts. The proposed vector-space graph representation model is based on the probabilistic apparatus of quantum Information Retrieval and on the compatibility of all operators representing subjects, predicates, objects and facts. The dual graph-vector representation of the available propositional data enables the entropy-reducing transformation and supports the compositionality of mutually compatible representations. As shown in the experiments presented in the paper, the compositionality of the vector-space representations allows an HPMPP-based recommendation system to identify which of the unknown facts having the triple form (user X, likes, item Y ) are the most likely to be true in a way that is both effective and, in contrast to methods proposed so far, fully automatic.

Open access

Andrzej Szwabe, Pawel Misiorek, Michal Ciesielczyk and Czeslaw Jedrzejek

Abstract

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.

Open access

Paweł Misiorek, Przemysław Walkowiak, Stanisław Karlik and Salvatore Vanini

Abstract

A growing number of mobile nodes that require Internet access is observed. These nodes may be organized in a wireless mesh network in which some of the nodes may serve the access to the Internet and relay other users’ traffic. Such a vision, however, causes the need for carrier-grade reliable Internet sharing solution. This paper presents a CARMNET-XML protocol, which enables to provide Authentication, Authorisation and Accounting AAA functionalities in wireless mesh networks managed by the Delay-Aware Network Utility Maximization System (DANUMS). The presented solution is a part of a CARMNET system, which integrates the utility-oriented resource allocation provided by DANUMS with the IMS architecture. The system allows users to access Internet with a given quality without the need of extending the operator’s infrastructure. Moreover, we define the scenario of the system application involving the use of the proposed protocol that has been experimentally evaluated in a wireless testbed environment