Objective
Bayesian nonparametric (BNP) methods have become very popular over recent years in machine learning and statistics as it allows to build elegant and sophisticated models. Contrary to Bayesian parametric methods, this set of techniques allows the number of parameters to grow with the number of data and is particularly suitable in the data rich environment we now face. This project aims at developing new Bayesian models for the probabilistic modeling of large and structured data such as networks and buyer preferences.
First, we aim at developing new models for networked data. The last few years have seen a tremendous interest in the study and understanding of complex networks. We plan to develop new models for static and dynamic networks, with or without clustering structure, that can handle a potentially large number of nodes and exhibit a power-law behavior, with simple inference procedures for the parameters.
Second, we aim at developing BNP recommender systems. Recommender systems aim at predicting the preference that a user would give to a specific item. They are especially useful for e-commerce in order to provide targeted advertisements to users. When the number of potential users and items is potentially large compared to the number of transactions, a BNP approach becomes sensible. We aim at developing new probabilistic models for the modeling of the behavior of buyers over time.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- social sciences economics and business business and management commerce e-commerce
- natural sciences mathematics applied mathematics statistics and probability
- natural sciences computer and information sciences artificial intelligence machine learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
FP7-PEOPLE-2012-IEF
See other projects for this call
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
OX1 2JD Oxford
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.