for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model. 1.

, to the "mix" of different applications used at a site, to the levels of congestion seen on different links. We discuss two key strategies for developing meaningful simulations in the face of these difficulties: searching for invariants, and judiciously exploring the simulation parameter space. We

.k.a. collaborative filtering) is attractive for many domains: books, CDs, movies, etc., but does not always work well. Because data are always sparse -- any given person has seen only a small fraction of all movies -- much more accurate predictions can be made by grouping people into clusters with similar movies

Many existing approaches to collaborative filtering can neither handle very large datasets nor easily deal with users who have very few ratings. In this paper we present the Probabilistic Matrix Factorization (PMF) model which scales linearly with the number of observations and, more importantly

parameter space. We finish with a look at a collaborative effort to build a common simulation environment for conducting Internet studies.

-based recommendations. We extend Hofmann’s (1999) aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not imposed as an exogenous parameter, but rather emerges naturally from the given data sources. However

Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships. A predominant approach to collaborative filtering is neighborhood based (“k-nearest neighbors”), where a user-item preference rating is interpolated from

We determine non-perturbatively the parameters of the lattice HQET Lagrangian and those of the time component of the heavy-light axial-vector current in the quenched approximation. The HQET expansion includes terms of order 1/mb. Our results allow to compute, for example, the heavy-light spectrum

Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP estimate of the model parameters, a procedure that can be performed efficiently even on very large datasets. However

A measurement of Do lifetime using the impact parameter method is presented. The Do sample is obtained from identified D* * decays in e+e- annihilations into hadrons at a center of mass energy of 29 GeV. The maximum likelihood method used includes the effects of various backgrounds, giving the Do