Jean Tague

Collaborative coefficient: A single measure of the degree of collaboration in research

It is shown that the mean number of authors per paper or the proportion of the multiple-authored papers is inadequate as a measure of the degree of collaboration in a discipline. A measure which combines some of the merits of both measures is suggested and derived. This measure, called the Collaborative Coefficient, is derived for four commonly used probability distributions.

Evaluation of the user interface in an information retrieval system: a model

Abstract Planning the evaluation of an information retrieval system involves a determination of the performance descriptors and measures appropriate to the system objectives and a development of an evaluation design that ensures the effect of variation in components of interest will be isolated and assessed in an unbiased fashion. This article presents an evaluation model that is appropriate to the assessment of the effect, from the perspective of the user, of variation in the interface to the system. The model is exemplified by an application to evaluation of an experimental online public-access catalog interface.

Complete formal model for information retrieval systems

What should the components of a model of an information retrieval system be? The answer to this question is not easy, because the term ‘information retrieval system’ has had a changing referent. In the early days of computer-based retrieval, information retrieval systems worked with document representations consisting of simple lists of keywords. Today, the information retrieved may be from full-text or hypertext or even hypermedia systems.

Split size-rank models for the distribution of index terms

Since the introduction of the Zipf distribution, many functions have been suggested for the frequency of words in text. Some of these models have also been applied to the distribution of index terms in a set of documents. The models are of two forms: rank‐frequency and frequency‐size. The former serve well to describe the distribution of high‐frequency terms; the latter the distribution of low‐frequency terms. In this article, a split model is proposed, which uses both a rank function for the high frequency terms and a size function for the low frequency terms, with the point of transition being determined either empirically or by rule. This model is fitted to the marginal empirical term distributions for four document datasets. Distributions to describe index term exhaustivity and term co‐occurrence are also considered briefly.

The maximal value of a Zipf size variable: sampling properties and relationship to other parameters

Abstract Because the Zipf size-frequency distribution is used so often as a mathematical model for bibliometric variables, it is important that the relationships among its parameters and its sampling properties be understood by investigators in this field. This paper examines these relationships and properties. In addition, it provides tables for the sampling distribution of the maximal value of a finite Zipf distribution and an approximation formula for confidence intervals. Confidence limits for the maximal value in a number of previous studies are determined.

Some measures and procedures for evaluation of the user interface in an information retrieval system

Planning the evaluation of an information retrieval system involves two steps: first, a determination of performance descriptors and measures appropriate to the system objectives and, secondly, a development of an evaluation design which ensures the effect of variation in components of interest will be isolated and assessed in an unbiased fashion. This paper examines the question of retrieval system evaluation from the perspective of the user. It presents evaluation procedures which are appropriate to this perspective and which can be used to isolate the effect of variation in the user interface to the system. The general procedure is exemplified by an application to evaluation of an experimental OPAC interface.

Simulation of user judgments in bibliographic retrieval systems

The general model and simulation algorithms for bibliographic retrieval systems presented in an earlier paper are expanded. The new model integrates the physical as well as the logical and semantic elements of these systems. A modified algorithm is developed for the simulation of user relevance judgments, and is validated, by means of recall-precision curves and a Kolmogorov-Smirnov test of recall, for two test collections. Other approaches to goodness-of-fit testing are suggested.

Morse's Markov Model of Book Use Revisited.

Over a decade ago, P. M. Morse suggested a simple Markov model of book use for a science library. Since then, his model has been modified and used in a number of applications, but the reliability of the basic assumptions underlying the models parameters has not been tested. Utilizing 11 years of circulation transactions of a university library, it is shown that (1) Morses model fits approximately 99% of the data for the whole collection and for three subject areas, and (2) contrary to his assumptions, one of the models two parameters, a, is time dependent.

A Bayesian approach to interactive retrieval

Abstract This paper presents a probabilistic model for interactive retrieval. Specifically, it applies the principles of Bayesian statistical decision theory : (1) the use of both prior (subjective) and sample information about the relationship of document descriptions to query relevance, and (2) maximization of the expected value of a utility function, to the problem of optimally restructuring a search strategy in an interactive environment.

Informativeness as an ordinal utility function for information retrieval

Information retrieval has all the elements of a classical decision problem: a set of possible actions, a set of potential states, and a reward or utility attached to each combination of action and state. How the actions, states, and utilities are described, however, is variable, and depends very much on the describers point of view.Information retrieval has all the elements of a classical decision problem: a set of possible actions, a set of potential states, and a reward or utility attached to each combination of action and state. How the actions, states, and utilities are described, however, is variable, and depends very much on the describers point of view.