We extended language modeling approaches in information retrieval (IR) to combine collaborative filtering (CF) and content-based filtering (CBF). Our approach is based on the analogy between IR and CF, especially between CF and relevance feedback (RF). Both CF and RF exploit users’ preference/relevance judgments to recommend items. We first introduce a multinomial model that combines CF and CBF...

SUMMARY This paper discusses the log-linear model for multi-way contingency table , where the cell values represent the frequency counts that follow an extended negative multinomial distribution. This is an extension of negative multinomial log-linear model described by Evans (1989). The parameters of the new model are estimated by maximum likelihood method. The likelihood ratio test for the ge...

The random coefficients multinomial choice logit model, also known as the mixed logit, has been widely used in empirical choice analysis for the last thirty years. We prove that the distribution of random coefficients in the multinomial logit model is nonparametrically identified. Our approach requires variation in product characteristics only locally and does not rely on the special regressors...

This paper introduces a fast Bayesian online expectation maximization (BOEM) algorithm for multinomial mixtures. Using some properties of the Dirichlet distribution, we derive expressions for adaptive learning rates that depend solely on the data and the prior’s hyperparameters. As a result, we avoid the problem of having to tune the learning rates using heuristics. In the application to multin...

Multinomial Data By KWANG WOO AHN and KUNG-SIK CHAN Department of Statistics and Actuarial Science, The University of Iowa, Iowa City, IA USA [email protected] [email protected] Summary We propose a new, block Gibbs sampling scheme for incomplete multinomial data. The new approach facilitates maximal blocking, thereby reducing serial dependency and speeding up the convergence of the ...

A challenge for the efficient operation of power systems and wind farms is the occurrence of wind power ramps, which are sudden large changes in the power output from a wind farm. This paper considers the probabilistic forecasting of a ramp event, defined as exceedance beyond a specified threshold. We directly model the exceedance probability using autoregressive logit models fitted to the chan...

This paper proposes a new semi-parametric identification and estimation approach to multinomial choice models in a panel data setting with individual fixed effects. Our approach is based on cyclic monotonicity, which is a defining feature of the random utility framework underlying multinomial choice models. From the cyclic monotonicity property, we derive identifying inequalities without requir...

We consider the problem of maximizing expected utility when utilities and probabilities are given by discrete probability distributions so that expected utility is a discrete stochastic variable. As for discrete second-order distributions, that is probability distributions where the variables are themselves probabilities, the multinomial family is a reasonable choice at least if first-order pro...

Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advoc...

Maximum likelihood estimation of mixed effect baseline category logit models for multinomial longitudinal data can be prohibitive due to the integral dimension of the random effects distribution. We propose to use multidimensional unfolding methodology to reduce the dimensionality of the problem. As a by-product, readily interpretable graphical displays representing change are obtained. The met...