Author name cluster
Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.
IS Journal 2009 Journal Article
A multidimensional ontology design paradigm based on Aristotelian definitions provides knowledge structure necessary for testing scientific theories that make probabilistic predictions.
IJCAI Conference 2005 Conference Paper
Many practical problems have random variables with a large number of values that can be hierarchically structured into an abstraction tree of classes. This paper considers how to represent and exploit hierarchical structure in probabilistic reasoning. We represent the distribution for such variables by specifying, for each class, the probability distribution over its immediate subclasses. We represent the conditional probability distribution of any variable conditioned on hierarchical variables using inheritance. We present an approach for reasoning in Bayesian networks with hierarchically structured variables that dynamically constructs a flat Bayesian network, given some evidence and a query, by collapsing the hierarchies to include only those values necessary to answer the query. This can be done with a single pass over the network. We can answer the query from the flat Bayesian network using any standard probabilistic inference algorithm such as variable elimination or stochastic simulation. The domain size of the variables in the flat Bayesian network is independent of the size of the hierarchies; it depends on how many of the classes in the hierarchies are directly associated with the evidence and query. Thus, the representation is applicable even when the hierarchy is conceptually infinite.
AAAI Conference 2004 Conference Paper
Collaborative filtering is a popular technique for recommending items to people. Several methods for collaborative filtering have been proposed in the literature and the quality of their predictions compared in empirical studies. In this paper, we argue that the measures of quality used in these studies are based on rather simple assumptions. We propose and apply additional measures for comparing the effectiveness of collaborative filtering methods which are grounded in decisiontheory. [keywords: information agents, human-computer interaction, recommender systems, evaluation]
UAI Conference 2003 Conference Paper
In this paper we examine the problem of inference in Bayesian Networks with discrete random variables that have very large or even unbounded domains. For example, in a domain where we are trying to identify a person, we may have variables that have as domains, the set of all names, the set of all postal codes, or the set of all credit card numbers. We cannot just have big tables of the conditional probabilities, but need compact representations. We provide an inference algorithm, based on variable elimination, for belief networks containing both large domain and normal discrete random variables. We use intensional (i.e., in terms of procedures) and extensional (in terms of listing the elements) representations of conditional probabilities and of the intermediate factors.
UAI Conference 2001 Conference Paper
Collaborative filtering is the process of making recommendations regarding the potential preference of a user, for example shopping on the Internet, based on the preference ratings of the user and a number of other users for various items. This paper considers collaborative filtering based on explicitmulti-valued ratings. To evaluate the algorithms, weconsider only {em pure} collaborative filtering, using ratings exclusively, and no other information about the people or items.Our approach is to predict a user's preferences regarding a particularitem by using other people who rated that item and other items ratedby the user as noisy sensors. The noisy sensor model uses Bayes' theorem to compute the probability distribution for the user'srating of a new item. We give two variant models: in one, we learn a{em classical normal linear regression} model of how users rate items; in another,we assume different users rate items the same, but the accuracy of thesensors needs to be learned. We compare these variant models withstate-of-the-art techniques and show how they are significantly better,whether a user has rated only two items or many. We reportempirical results using the EachMovie database footnote{ http://research.compaq.com/SRC/eachmovie/} of movie ratings. Wealso show that by considering items similarity along with theusers similarity, the accuracy of the prediction increases.