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

Collaborative Filtering Based on Bi-Relational Data Representation

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

{"article-title":"Collaborative Filtering Based on Bi-Relational Data Representation"}

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...

Collaborative Filtering Based on Bi-Relational Data Representation

Publié en ligne: 23 févr. 2013

Pages: 67 - 83

DOI: https://doi.org/10.2478/v10209-011-0021-x

Mots clés
collaborative filtering, personalized recommendation, Statistical Relational Learning, RDF, propositional data representation

This content is open access.

Collaborative Filtering Based on Bi-Relational Data Representation

Publié en ligne: 23 févr. 2013

Pages: 67 - 83

DOI: https://doi.org/10.2478/v10209-011-0021-x

Mots cléscollaborative filtering, personalized recommendation, Statistical Relational Learning, RDF, propositional data representation

This content is open access.

Mots clés
collaborative filtering, personalized recommendation, Statistical Relational Learning, RDF, propositional data representation