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Paper details
Number 3 - September 2015
Volume 25 - 2015
A multi-agent brokerage platform for media content recommendation
Bruno Veloso, Benedita Malheiro, Juan Carlos Burguillo
Abstract
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.
Keywords
multi-agent computing, brokerage platform, media content personalisation, recommendation