<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

Holistic Entropy Reduction for Collaborative Filtering

Institute of Control and Information Engineering, Poznan University of Technology, M. Sklodowskiej-Curie Square 5, 60-965 Poznan

Institute of Control and Information Engineering, Poznan University of Technology, M. Sklodowskiej-Curie Square 5, 60-965 Poznan, Poland

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Holistic Entropy Reduction for Collaborative Filtering"}

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 )...

Holistic Entropy Reduction for Collaborative Filtering

Published Online: Sep 09, 2014

Page range: 209 - 229

Received: Nov 01, 2012

DOI: https://doi.org/10.2478/fcds-2014-0012

Keywords
collaborative filtering, self-configuration, propositional RDF-compliant data representation, quantum IR, information theory

© by Andrzej Szwabe

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Holistic Entropy Reduction for Collaborative Filtering

Published Online: Sep 09, 2014

Page range: 209 - 229

Received: Nov 01, 2012

DOI: https://doi.org/10.2478/fcds-2014-0012

Keywordscollaborative filtering, self-configuration, propositional RDF-compliant data representation, quantum IR, information theory

© by Andrzej Szwabe

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
collaborative filtering, self-configuration, propositional RDF-compliant data representation, quantum IR, information theory