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Showing papers on "Ranking (information retrieval) published in 1970"



Journal ArticleDOI
TL;DR: In this paper, an analysis of interaction structure in the discussion of a ranking task is presented, where the authors focus on the interaction structure of the ranking task and its interaction structure.
Abstract: (1970). An analysis of interaction structure in the discussion of a ranking task. Speech Monographs: Vol. 37, No. 4, pp. 249-256.

39 citations



Journal ArticleDOI
John H. Williams1
TL;DR: The objective of the development of this system, BROWSER, was to investigate the effectiveness of a free-form query with a combinatorial search algorithm and the effectivenessof various techniques and components to facilitate online browsing.
Abstract: : An effective man-machine interactive retrieval system is not achieved by simply placing a terminal on each end of an existing machine retrieval system. An interactive system requires a sequence of steps in which man and machine alternately take action. It should also provide different levels of services to experienced and inexperienced searches, recognize the difference between a narrow and broad query, furnish clues as to the next direction to be searched, reorganize the data base dynamically as the searcher changes his view- point, providing a ranking of responses in the most likely sequence and offer the searcher the option of overriding the ranking when a particular term is of extreme significance. An online interactive system meeting many of these needs was developed and tested. The objective of the development of this system, BROWSER, was to investigate the effectiveness of a free-form query with a combinatorial search algorithm and the effectiveness of various techniques and components to facilitate online browsing.

15 citations


Journal ArticleDOI
TL;DR: It is estimated that if the average patron's nonstandard information were exploited by appropriate retrieval strategies, he would only have to search through roughly one five-hundredth as many documents as in a random search, which is great enough to make non standard information potentially useful in many situations.
Abstract: Library patrons who wish to obtain a known document with which they have had prior personal contact often cannot remember the standard author-title-subject information about it with sufficient accuracy to be able to look it up easily in present-day card catalogs. However, they may remember a surprising amount of such “nonstandard” information as the color of the document's cover or its approximate length. Could this type of “nonstandard” information be profitably exploited in computerized catalogs of the future? Such facilities, if available, would surely be used, for according to catalog usage surveys a majority of today's library patrons seek known documents, and of these, a substantial minority possess nonstandard information. In this report, the results of a memory experiment to test the memorability of various types of nonstandard information are described and analyzed. A ranking according to relative memorability and potential retrieval usefulness of various nonstandard book features is given. It is estimated that if the average patron's nonstandard information were exploited by appropriate retrieval strategies, he would only have to search through roughly one five-hundredth as many documents as in a random search. This reduction factor is great enough to make nonstandard information potentially useful in many situations.

8 citations




Journal ArticleDOI
TL;DR: This paper examines and implements alternative algorithms to the Rank permutations and NthPermutations already contained in the standard add-on Mathematica packages, and provides some applications of these ranking procedures in different topics such as probability, statistics and elementary calculus.
Abstract: Permutation theory has many applications in several fields of science and technology and it also has a charm in itself. Mathematica is particularly suitable for writing combinatorial algorithms because it provides many easy-to-use tools for handling lists. Several combinatorial built-in functions which involve permutations and combinations are available as standard add-on Mathematica packages. They are grouped under the name of DiscreteMath and are described by their author Steven Skiena in his book [7]. In this paper we focus on ranking and unranking procedures and we examine and implement alternative algorithms to the RankPermutations and NthPermutations already contained in the above mentioned add-on packages. Moreover we provide some applications of these ranking procedures in different topics such as probability, statistics and elementary calculus. Let /„={(), 1, ..., n-\\ } be the set of the nonnegative integers smaller than n and let A be a rc-set. We denote the set of all permutations of A by the n!-set:

1 citations




01 Jan 1970
TL;DR: The findings indicate and confirm that different search tools on the web make use of different ranking algorithms, and supports findings of previous studies.
Abstract: One of evaluation studies examining web search tools is ranking algorithm area. Accepting the idea that different search tools do use different ranking algorithms, the present research aims to confirm such an idea using a statistical approach. To do this, five metasearch engines (MSEs) namely Ez2find, Dogpile, MetaCrawler, Info and WebCrawler along with their four common underlying single search engines (SEs) – Google, Yahoo!, Msn (currently called Windows Live Search) and Ask – have been applied. To conduct the research five queries have been utilized. For comparing ranking algorithms of these web search tools, statistical tests "Kruskal-Wallis" and "Tukey HSD" were utilized. The findings indicate and confirm that different search tools on the web make use of different ranking algorithms. In other words, this research supports findings of previous studies.