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 ) triples. The method involves the application of a novel self-configuration technique for the generation of vector-space representations optimi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2014-07-01
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| Series: | Foundations of Computing and Decision Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/fcds-2014-0012 |
| _version_ | 1811321221776670720 |
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| author | Szwabe Andrzej Misiorek Pawel Janasiewicz Tadeusz Walkowiak Przemyslaw |
| author_facet | Szwabe Andrzej Misiorek Pawel Janasiewicz Tadeusz Walkowiak Przemyslaw |
| author_sort | Szwabe Andrzej |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T13:13:03Z |
| format | Article |
| id | doaj.art-89ea846a034440838e944a5dfad1ff28 |
| institution | Directory Open Access Journal |
| issn | 2300-3405 |
| language | English |
| last_indexed | 2024-04-13T13:13:03Z |
| publishDate | 2014-07-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Foundations of Computing and Decision Sciences |
| spelling | doaj.art-89ea846a034440838e944a5dfad1ff282022-12-22T02:45:33ZengSciendoFoundations of Computing and Decision Sciences2300-34052014-07-0139320922910.2478/fcds-2014-0012fcds-2014-0012Holistic Entropy Reduction for Collaborative FilteringSzwabe Andrzej0Misiorek Pawel1Janasiewicz Tadeusz2Walkowiak Przemyslaw3Institute of Control and Information Engineering, Poznan University of Technology, M. Sklodowskiej-Curie Square 5, 60-965 PoznanInstitute of Control and Information Engineering, Poznan University of Technology, M. Sklodowskiej-Curie Square 5, 60-965 Poznan, PolandInstitute of Control and Information Engineering, Poznan University of Technology, M. Sklodowskiej-Curie Square 5, 60-965 Poznan, PolandInstitute of Control and Information Engineering, Poznan University of Technology, M. Sklodowskiej-Curie Square 5, 60-965 Poznan, PolandWe 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.https://doi.org/10.2478/fcds-2014-0012collaborative filteringself-configurationpropositional rdf-compliant data representationquantum irinformation theory |
| spellingShingle | Szwabe Andrzej Misiorek Pawel Janasiewicz Tadeusz Walkowiak Przemyslaw Holistic Entropy Reduction for Collaborative Filtering Foundations of Computing and Decision Sciences collaborative filtering self-configuration propositional rdf-compliant data representation quantum ir information theory |
| title | Holistic Entropy Reduction for Collaborative Filtering |
| title_full | Holistic Entropy Reduction for Collaborative Filtering |
| title_fullStr | Holistic Entropy Reduction for Collaborative Filtering |
| title_full_unstemmed | Holistic Entropy Reduction for Collaborative Filtering |
| title_short | Holistic Entropy Reduction for Collaborative Filtering |
| title_sort | holistic entropy reduction for collaborative filtering |
| topic | collaborative filtering self-configuration propositional rdf-compliant data representation quantum ir information theory |
| url | https://doi.org/10.2478/fcds-2014-0012 |
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