1,721,049 research outputs found
Dal Collaborative Filtering alla Cultura Implicita: definizione e sviluppo di un sistema di supporto
No full textTrust-aware Decentralized Recommender Systems: PhD research proposal
No full textThis PhD thesis addresses the following problem: exploiting of trust information in order to enhance the accuracy and the user acceptance of current Recom- mender Systems (RS). RSs suggest to users items they will probably like. Up to now, current RSs mainly gener- ate recommendations based on users` opinions on items. Nowadays, with the growth of online communities, e-marketplaces, weblogs and peer-to-peer networks, a new kind of information is available: rating expressed by an user on another user (trust). We analyze current RS weaknesses and show how use of trust can overcome them. We proposed a solution about exploiting of trust into RSs and underline what experiments we will run in order to test our solutio
Image reconstruction methods for Solar Orbiter STIX
No full textThe Spectrometer/Telescope for Imaging X-rays (STIX) is the instrument of the Solar Orbiter mission conceived for the observation of the X-ray radiation emitted during solar flares. STIX adopts an indirect imaging technique based on the use of modulation collimators, i.e., pairs of grids mounted in front of detectors. By measuring the modulated X-ray photon flux, the telescope provides the complex values of 30 Fourier components of the angular distribution of the X-ray source, named visibilities. Hence, the imaging problem for STIX is the inverse problem of reconstructing the image of the flaring X-ray source from a sparse sampling of its Fourier transform.
In this thesis, we describe the STIX imaging concept and the image reconstruction problem both from visibilities and from photon count measurements. We present different image reconstruction techniques: the count-based Expectation Maximization (EM) algorithm, the visibility-based maximum entropy method MEM_GE and a neural network approach for parametric image reconstruction. We test these methods on synthetic STIX data and, in the case of MEM_GE, on real data provided by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). We propose a stopping rule for EM based on a predictive risk estimator. This estimator is derived by using a Poisson counterpart of Stein’s lemma for Gaussian variates. We validate the performance of the stopping rule in the case of the STIX imaging problem from synthetic data and in the case of a deconvolution inverse problem. Finally, we present the results of the STIX imaging problem from real data. We describe the first approaches of image reconstruction from semi-calibrated data, i.e., visibility amplitudes only. Then, we provide an overview of the visibility phase calibration, and we show the reconstructions from fully-calibrated visibilities obtained by using several methods, including MEM_GE and EM. The STIX reconstructions are validated by comparison with maps of the same events provided by the Atmospheric Imaging Assembly on board the Solar Dynamic Observatory (SDO/AIA) in UV wavelengths
Predictive risk estimation for the expectation maximization algorithm with Poisson data
No full textIn this work, we introduce a novel estimator of the predictive risk with Poisson data, when the loss function is the Kullback–Leibler divergence, in order to definearegularizationparameter’s choicerule for theexpectationmaximization (EM) algorithm. To this aim, we provea Poisson counterpartof the Stein’s Lemma for Gaussian variables, and from this result we derive the proposed estimator showing its analogies with the well-known Stein’s unbiased risk estimatorvalid for a quadraticloss. We provethat the proposedestimator is asymptotically unbiased with increasing number of measured counts, under certain mildconditionsontheregularizationmethod.We showthattheseconditionsare satisfied by the EM algorithm under the hypothesis that the underlying matrix has positive entries and then we apply this estimator to select the EM optimal reconstruction. We present some numerical tests in the case of image deconvolution, comparing the performances of the proposed estimator with other methods available in the literature, both in the inverse crime and non-inverse crime setting
Wiki Revolutions: Wikipedia as a Lens for Studying the Real-Time Formation of Collective Memories of Revolutions
No full text
Trust-aware bootstrapping of recommender systems
No full textRecommender Systems (RS) suggest to users items they
might like such as movies or songs. However they are not able to generate recommendations for users who just registered, in fact bootstrapping Recommender Systems for new users is still an open challenge. While traditional RSs exploit only ratings provided by users about items, Trust-aware Recommender Systems let the user express also trust statements, i.e. their subjective opinions about the usefulness of other users. In this paper we analyze the relative benefits of asking new users either few ratings about items or few trust statements about other users for the purpose of bootstrapping a RS ability to generate recommendations. We run experiments on a large real world dataset derived from the online Web community of Epinions.com. The results clearly indicate that while traditional RS algorithms exploiting ratings on items fail for new users, asking few trust statements to a new user is instead a very effective strategy able to quickly let the RS generate many accurate items recommendations
Trustlet, Open Research on Trust Metrics
No full textA trust metric is a technique for predicting how much a user
of a social network might trust another user. This is especially beneficial in situations where most users are unknown to each other such as online communities. We think the recent tumultuous evolution of social networking demands for a collective research effort. With this in mind we created Trustlet.org, a platform consisting of a wiki for open research on trust metrics. The goal of Trustlet is to collect and distribute trust network datasets and trust metrics code as free software, in order to facilitate the comparison of different trust metrics algorithms and a more coherent progress in this field. At present we made available some social network datasets and code for some trust metrics. In this paper we also report a first empirical evaluation of different trust metrics on the Advogato social network dataset
- …
