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A multi-agent brokerage platform for media content recommendation

Veloso B., Malheiro B., Burguillo J. C.

International Journal of Applied Mathematics and Computer Science

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2015

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Vol. 25, no. 3

513--527

EN

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.

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Urbancontext: a management model for pervasive environments in user-oriented urban computing

Zuniga-Canon C. L., Burguillo J. C.

Computer Science

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2014

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Vol. 15 (1)

75--88

EN

Nowadays, urban computing has gained a lot of interest in regards to guiding the evolution of cities into intelligent environments. These environments can be appropriated for interactions between individuals that may ultimately affect and modify their behavior. These changes require new approaches that allow us to better understand how urban computing systems should be modeled. In this work, we present UrbanContext – a new model for designing urban computing platforms that applies the theory of roles to manage the individual’s context in urban environments. The theory of roles helps us understand the individual’s behavior within a social environment, allowing us to model urban computing systems capable of adapting to an individual’s states and needs. In order to optimize social interaction and offer secure services, Urban-Context collects data in urban atmospheres and classifies behavior according to the individual’s change of roles. Likewise, UrbanContext serves as a generic model to provide interoperability as well as facilitate design, implementation, and expansion of urban computing systems.

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Using tags in an AIML-based chatterbot to improve its knowledge

Mikic F. A., Burguillo J. C., Peleteiro A., Rey-Lopez M.

Computer Science

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2012

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Vol. 13 (2)

123-133

EN

Nowadays, it is common to find on the Internet different conversational robots which interact with users simulating a natural language conversation. Among them, we can emphasize the chatterbots based on AIML language. In this paper we present an AIML based chatterbot that shows as its main contribution the use of tags and folksonomies. Thanks to its use, we can generate a context for each conversation, being able to maintain a state for each user in the system, and improving the adaptation capabilities of the bot.