Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

PAAL-POC

Financiado

Practical Approximation Algorithms Proof of Concept

Nowadays, we witness that more and more information is stored and managed in a digital way. Moreover, very often processes are executed and planed by computers. This allows applying computer methods to optimize performance of our... ver más

30/04/2017

UNIWARSAW

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

UNIWERSYTET WARSZAWSKI No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2017-04-30

ERC-PoC-2015: ERC Proof of Concept Grant

I+D

Cerrada hace 9 años

0% 100% 100%

2 Participantes

UNIWARSAW

150.00K€ | Lider

MAGNETI MARELLI TUBOS DE ESC...

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Proyectos interesantes

FJC2021-047112-I Fuzzy systems on time series analysis, modeling, and forecas... 65K€ Cerrado

RYC-2010-06984 Fuzzy Modelling and Rough Sets for Recommender Systems and D... 192K€ Cerrado

Análisis de pedidos del cliente mediante Mach... Análisis de pedidos del cliente mediante Machine Learning y... Rechazado

TIN2012-32682 AUMENTO DE PRESTACIONES EN LOS SISTEMAS DE RECOMENDACION BAS... 14K€ Cerrado

TIN2015-64282-R MODELOS DE LENGUAJE PROBABILISTICOS PARA RANKINGS PERSONALIZ... 60K€ Cerrado

PID2019-106493RB-I00 AUMENTO DE LA CALIDAD Y DE LA EQUIDAD, A GRUPOS MINORITARIOS... 28K€ Cerrado

Duración del proyecto: 18 meses Fecha Inicio: 2015-10-20
Fecha Fin: 2017-04-30

Líder del proyecto

UNIWERSYTET WARSZAWSKI No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 150K€

Descripción del proyecto Nowadays, we witness that more and more information is stored and managed in a digital way. Moreover, very often processes are executed and planed by computers. This allows applying computer methods to optimize performance of our actions on an unprecedented scale. This is clearly visible in the case of eCommerce, where the main arena of operation of companies is handled solely using computers. Typically, machine learning tools and algorithms are widely used, e.g., for the prediction of user behavior, user classification, or in recommendation systems. When applying such tools one needs to base his computations on existing historical data. This limits the prediction power of such systems, as we cannot predict the reaction of the users nor of the markets to changes in our strategy. In the case of bidding for Ads in online auctions, we only have full information about the auctions we have won, but in the case of lost auctions we only know that we have lost. Hence, it is almost impossible to predict which auctions we would win using only plain historical data. This problem calls for a novel approach that could extrapolate missing information. Here, we propose the development of such framework together with the programming library that would support such extrapolation. This new framework will incorporate algorithmic game theory into the existing approximation and machine learning algorithms. Game theory gives the right tools to talk about incentives of strategic agents and allows predicting response of market actors to changing conditions. Our idea is to describe these incentives and to build a force feedback loop between market models and algorithmic optimization methods. We will first extract and learn the parameters of the market models from the historical data, only then the extrapolated model will be used as the benchmark for the optimization methods. This novel idea will allow to use optimization tools in the previously intractable parameter range.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores