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An Instance of Social Intelligence in the Internet of Things: Bread Making Recipe Recommendation by ELM Regression

  • Manuel Graña
  • J. David Nuñez-GonzalezEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9121)

Abstract

The Social and Smart project proposes a new framework for the interaction between users and their household appliances, where social interaction becomes an intelligent social network of users and appliances which is able to provide intelligent responses to the needs of the users. In this paper we focus on one incrasingly common appliance in the european homes: the bread-maker. There are a number of satisfaction parameters which can be specified by the user: crustiness, fragance, baking finish, and softness. A bread making recipe is composed mainly of the temperatures and times for each of the baking stages: first leavening, second leavening, precooking, cooking and browning. Although a thoroughful real life experimentation and data collection is being carried out by project partners, there are no data available for training/testing yet. Thus, in order to test out ideas we must resort to synthetic data generated using a very abstract model of the satisfaction parameters resulting from a given recipe. The recommendation in this context is carried by a couple of Extreme Learning Machine (ELM) regression models trained to predict the satisfaction parameters from the recipe input, and the other the inverse mapping from the desired satisfaction to the breadmaker appliance recipe. The inverse map allows to provide recommendations to the user given its preferences, while the direct map allows to evaluate a recipe predicting user satisfaction.

Keywords

Recommender System Extreme Learn Machine User Satisfaction Hide Unit Social Player 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research has been partially funded by EU through SandS project, grant agreement no 317947. The GIC has been supported by grant IT874-13 as university research group category A.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Computational Intelligence GroupUPV/EHULeioaSpain

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