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Social networks enable smart household appliances to make better recommendations

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The thesis entitled Computational intelligent methods for trusting in social networks, produced by the computer engineer David Núñez in the Computational Intelligence Group at the UPV/EHU's Faculty of Computing, falls within the framework of the European research project Social and Smart (SandS). A part of the project is focussing its attention on user interaction with smart domestic appliances linked to a smart module.

These are household appliances (systems) to which the user describes in ordinary language the problem that he/she wants to solve (such as "making bread", "removing a stain from a pair of trousers", etc. depending on the type of household appliance); the system analyses the problem that needs to be solved and searches the database to see whether there is a solution (recipe) for the problem described by the user. If one exists, it is provided, and if not, the system will forward the description of the problem to an intelligent module so that a new solution can be produced and then passed on to the user. The user can execute the proposed solution or else readjust its parameters. Once the execution of the problem has been completed, the user will express his/her satisfaction with the result obtained. The users can communicate with each other over the system's social network and propose recipes that can be evaluated by other users.

The thesis by Núñez has provided new intelligent techniques in the area of social networks. Specifically, he has covered three lines of research in this area: trust, the recommendation systems and the maximising of influence.

Three lines of research

The first line of research seeks to predict the trust that a user will place in another belonging to his/her social environment on the basis of the opinions that other contacts have expressed about the target user. In this line the researcher has managed to develop some tools for predicting trust that are more straightforward than the ones found in the literature and more algebra-based.

The second line of research focusses on the systems of recommendation, and two experiments have been carried out. The first is linked to the generating of recipes for making bread in a smart bread maker. An attempt has been made to simulate the prediction of the bread recipe (solution of the problem) on the basis of the satisfaction expressed (description of the problem), and even, in the opposite direction, to predict satisfaction (solution of the problem) on the basis of a recipe provided (description of the problem). The second task in this second line of research has endeavoured to make recommendations about products. The recommendation is based on the previous evaluations of the users. What is being proposed are techniques based on the Web of Trust of the target user to whom one wishes to make a recommendation and also on similarities between users and the means of evaluation they have.

The third line of research is related to maximising influence. The aim of this line has been to detect what would be the minimum set of users of a social network that is capable of influencing the maximum possible number of users of the network. In this respect, "we have come up with a new algorithm that improves the algorithm that exists in the literature in terms of time: the classical Greedy method," explained David Núñez. "Our method has succeeded in getting closer to the optimum like the Greedy one, but does so more rapidly".

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Additional information

The Social and Smart European project has been funded by the FP7 framework programme, and the consortium of this project comprises three universities and five European companies and R&D centres: the University of Milan (project coordinator), the UPV/EHU, National Polytechnic University of Athens, Cartif (Valladolid), Libelium (Zaragoza), Gorenje, Arduino and Amis.

David Núñez (Irún, 1988) is a computer engineer. He has done a University Master's in Computational Engineering and Smart Systems and a Teaching Master's in Secondary Education. He wrote up his PhD thesis in the Department of Computation Science and Artificial Intelligence at the Computing Faculty in Donostia-San Sebastian, under the supervision of Prof Manuel Graña.

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Naiara Billalabeitia
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