The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple users to recommend items of interest to other users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed and evaluated many approaches for generating recommendations. We describe and evaluate a new method called personality diagnosis (PD). Given a user's preferences for some items, we compute the probability that he or she is of the same "personality type" as other users, and, in turn, the probability that he or she will like new items. PD retains some of the advantages of traditional similarity-weighting techniques in that all data is brought to bear on each prediction and new data can be added easily and incrementally. Additionally, PD has a meaningful probabilistic interpretation, which ma...

Decision trees have proved to be valuable tools for the description, classification and generalization of data. Work on constructing decision trees from data exists in multiple disciplines such as statistics, pattern recognition, decision theory, signal processing, machine learning and artificial neural networks. Researchers in these disciplines, sometimes working on quite different problems, identified similar issues and heuristics for decision tree construction. This paper surveys existing work on decision tree construction, attempting to identify the important issues involved, directions the work has taken and the current state of the art. Keywords: classification, tree-structured classifiers, data compaction 1. Introduction Advances in data collection methods, storage and processing technology are providing a unique challenge and opportunity for automated data exploration techniques. Enormous amounts of data are being collected daily from major scientific projects e.g., Human Genome...

Recommender systems have become valuable resources for users seeking intelligent ways to search through the enormous volume of information available to them. One crucial unsolved problem for recommender systems is how best to learn about a new user. In this paper we study six techniques that collaborative filtering recommender systems can use to learn about new users. These techniques select a sequence of items for the collaborative filtering system to present to each new user for rating. The techniques include the use of information theory to select the items that will give the most value to the recommender system, aggregate statistics to select the items the user is most likely to have an opinion about, balanced techniques that seek to maximize the expected number of bits learned per presented item, and personalized techniques that predict which items a user will have an opinion about. We study the techniques thru offline experiments with a large preexisting user data set, and thru a live experiment with over 300 users. We show that the choice of learning technique significantly affects the user experience, in both the user effort and the accuracy of the resulting predictions.

Automated problem solving is viewed typically as the allocation of computational resources to solve one or more problems passed to a reasoning system. In response to each problem received, effort is applied in real time to generate a solution and problem solving ends when a solution is rendered. We examine continual computation, reasoning policies that capture a broader conception of problem by considering the proactive allocation of computational resources to potential future challenges. We explore policies for allocating idle time for several settings and present applications that highlight opportunities for harnessing continual computation in real-world tasks. 2001 Elsevier Science B.V. All rights reserved. Keywords: Bounded rationality; Decision-theoretic control; Metareasoning; Deliberation; Compilation; Speculative execution; Value of computation 1.

The belief network framework for reasoning with uncertainty in knowledgebased systems has been around for some time now. As more and more practical applications employing the framework are being developed, it becomes apparent that the framework lacks with regard to explicit means for exerting control over reasoning. In this paper, we extend the belief network framework with a method for selective gathering of evidence for diagnostic applications. To this end, a belief network architecture is developed consisting of two layers: a probabilistic layer specifying a belief network and its associated algorithms, and a control layer providing the method for evidence gathering. 1 Introduction Halfway through the 1980s, the theory of belief networks was introduced for reasoning with uncertainty in knowledge-based systems. The belief network framework provides a formalism for representing knowledge concerning a joint probability distribution on a set of variables discerned in a domain, and in a...

In this paper we discuss applications of Bayesian networks to educational testing.

Intensive computations required for sensing and processing perceptual information can impose significant burdens on personal computer systems. We explore several policies for selective perception in SEER, a multimodal system for recognizing o#ce activity that relies on a layered Hidden Markov Model representation. We review our e#orts to employ expected-value-of-information (EVI) computations to limit sensing and analysis in a context-sensitive manner. We discuss an implementation of a one-step myopic EVI analysis and compare the results of using the myopic EVI with a heuristic sensing policy that makes observations at di#erent frequencies. Both policies are then compared to a random perception policy, where sensors are selected at random. Finally, we discuss the sensitivity of ideal perceptual actions to preferences encoded in utility models about information value and the cost of sensing.

drHugin, which is an extension of Hugin, deals with value of information in Bayesian networks. The approach is constructed for situations where one variable is in the focus of interest, and the probability distribution for that variable drives the system. In this framework both utility based and utility free assessments of information sources are available. Keywords Bayesian Networks, Data Request, Utility-based Values, Utility-free Values, Value of information. 1 Introduction Whenever decisions under uncertainty are to be made, there is a quest for more information to reduce the uncertainty. However, information is rather seldom cost free, and therefore there is also a need for evaluating on beforehand whether it is worthwhile to consult an information source. Furthermore, if several sources are available there is a need to come up with a strategy for a sequence of data requests. The problem of data request has been formally treated in decision theory (Howard 1966, Lindley 1971, ...

Decision Trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining considered the issue of growing a decision tree from available data. This paper presents an updated survey of current methods for constructing decision tree classifiers in top-down manner. The paper suggests a unified algorithmic framework for presenting these algorithms and provides profound descriptions of the various splitting criteria and pruning methodology.

In artificial intelligence research, the belief network framework for automated reasoning with uncertainty is rapidly gaining in popularity. The framework provides a powerful formalism for representing a joint probability distribution on a set of statistical variables.