Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/6651
Título: Brokerage Platform for Media Content Recommendation
Autor: Veloso, Bruno
Malheiro, Benedita
Burguillo, Juan Carlos
Palavras-chave: Multi-agent computing
Brokerage platform
Media content personalisation
Recommendation
Data: 2015
Editora: University of Zielona Góra - Institute of Control and Computation Engineering
Citação: Bruno Veloso; Benedita Malheiro; Juan Carlos Burguillo. Brokerage Platform for Media Content Recommendation, International Journal of Applied Mathematics and Computer Science, 25, 3, 513-527, 2015.
Relatório da Série N.º: AMCS;Vol. 25, nº 3
Resumo: Near real time media content personalisation is nowadays a major challenge involving media content sources, distributors and viewers. This paper describes an approach to seamless recommendation, negotiation and transaction of personalised media content. It adopts an integrated view of the problem by proposing, on the business-to-business (B2B) side, a brokerage platform to negotiate the media items on behalf of the media content distributors and sources, providing viewers, on the business-to-consumer (B2C) side, with a personalised electronic programme guide (EPG) containing the set of recommended items after negotiation. In this setup, when a viewer connects, the distributor looks up and invites sources to negotiate the contents of the viewer personal EPG. The proposed multi-agent brokerage platform is structured in four layers, modelling the registration, service agreement, partner lookup, invitation as well as item recommendation, negotiation and transaction stages of the B2B processes. The recommendation service is a rule-based switch hybrid filter, including six collaborative and two content-based filters. The rule-based system selects, at runtime, the filter(s) to apply as well as the final set of recommendations to present. The filter selection is based on the data available, ranging from the history of items watched to the ratings and/or tags assigned to the items by the viewer. Additionally, this module implements (i) a novel item stereotype to represent newly arrived items, (ii) a standard user stereotype for new users, (iii) a novel passive user tag cloud stereotype for socially passive users, and (iv) a new content-based filter named the collinearity and proximity similarity (CPS). At the end of the paper, we present off-line results and a case study describing how the recommendation service works. The proposed system provides, to our knowledge, an excellent holistic solution to the problem of recommending multimedia contents.
URI: http://hdl.handle.net/10400.22/6651
DOI: 10.1515/amcs-2015-0038
ISSN: 2083-8492
Versão do Editor: https://www.amcs.uz.zgora.pl/?action=paper&paper=840
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