Similar concepts
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structures are decomposable
...A further simplification of the measurement function may be achieved by requiring a special kind of non interaction of the components which has become known as additive independence
...R,P >R,P <>[[Phi]]1 R [[Phi]]2 P >[[Phi]]1 R [[Phi]]2 P where F is simply the addition function
...R,P >R,P <>[[Phi]]1 R [[Phi]]2 P [[Phi]]1 R [[Phi]]2 P >[[Phi]]1 R [[Phi]]2 P [[Phi]]1 R [[Phi]]2 P It can be shown that starting at the other end given an additively independent representation the properties defined in 1 and 3,and the Archimedean property are necessary
...Here the term [[Phi]]1 [[Phi]]2 is referred to as the interaction term,its absence accounts for the non interaction in the previous condition
...We are now in a position to state the main representation theorem
...Theorem Suppose <R x P,>>is an additive conjoint structure,then there exist functions,[[Phi]]1 from R,and [[Phi]]2 from P into the real numbers such that,for all R,R [[propersubset]]R and P,P [[propersubset]]P:R,P >R,P <>[[Phi]]1 R [[Phi]]2 P >[[Phi]]1 R [[Phi]]2 P If [[Phi]]i []are two other functions with the same property,then there exist constants [[Theta]]>0,[[gamma]]1,and [[gamma]]2 such that [[Phi]]1 [][[Theta]][[Phi]]1 [[gamma]]1 [[Phi]]2 [][[Theta]][[Phi]]2 [[gamma]]2 The proof of this theorem may be found in Krantz et al
...Let us stop and take stock of this situation
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automatic and interactive retrieval system?Studies to gauge this are going on but results are hard to interpret
...It should be apparent now that in evaluating an information retrieval system we are mainly concerned with providing data so that users can make a decision as to 1 whether they want such a system social question and 2 whether it will be worth it
...The second question what to evaluate?boils down to what can we measure that will reflect the ability of the system to satisfy the user
...1 The coverage of the collection,that is,the extent to which the system includes relevant matter;2 the time lag,that is,the average interval between the time the search request is made and the time an answer is given;3 the form of presentation of the output;4 the effort involved on the part of the user in obtaining answers to his search requests;5 the recall of the system,that is,the proportion of relevant material actually retrieved in answer to a search request;6 the precision of the system,that is,the proportion of retrieved material that is actually relevant
...It is claimed that 1 4 are readily assessed
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In basing a theory of evaluation on the theory of measurement,is it possible to devise a measure of effectiveness not starting with precision and recall but simply with the set of relevant documents and the set of retrieved documents?If so,can we generalise such a measure to take account of degree of relevance?An alternative derivation of an E type measure could be done in terms of recall and fallout
...Up to now the measurement of effectiveness has proved fairly intractable to statistical analysis
...I think the Robertson model described in Chapter 7 goes some way to being considered as a reasonable statistical model
...There may be laws of retrieval such as the well known trade off between precision and recall that are worth establishing either empirically or by theoretical argument
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...There is a need for more intensive research into the problems of what to use to represent the content of documents in a computer
...Information retrieval systems,both operational and experimental,have been keyword based
...The major reason for this rather simple minded approach to document retrieval is a very good one
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In the past there has been much debate about the validity of evaluations based on relevance judgments provided by erring human beings
...Effectiveness and efficiency Much of the research and development in information retrieval is aimed at improving the effectiveness and efficiency of retrieval
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subsets differing in the extent to which they are about a word w then the distribution of w can be described by a mixture of two Poisson distributions
...here p 1 is the probability of a random document belonging to one of the subsets and x 1 and x 2 are the mean occurrences in the two classes
...Although Harter [31]uses function in his wording of this assumption,I think measure would have been more appropriate
...assumption 1 we can calculate the probability of relevance for any document from one of these classes
...that is used to make the decision whether to assign an index term w that occurs k times in a document
...Finally,although tests have shown that this model assigns sensible index terms,it has not been tested from the point of view of its effectiveness in retrieval
...Discrimination and or representation There are two conflicting ways of looking at the problem of characterising documents for retrieval
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In practice,one seeks some sort of optimal trade off between representation and discrimination
...The emphasis on representation leads to what one might call a document orientation:that is,a total preoccupation with modelling what the document is about
...This point of view is also adopted by those concerned with defining a concept of information,they assume that once this notion is properly explicated a document can be represented by the information it contains [37]...The emphasis on discrimination leads to a query orientation
...Automatic keyword classification Many automatic retrieval systems rely on thesauri to modify queries and document representatives to improve the chance of retrieving relevant documents
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the system to its user will be the best that is obtainable on the basis of those data
...Of course this principle raises many questions as to the acceptability of the assumptions
...The probability ranking principle assumes that we can calculate P relevance document,not only that,it assumes that we can do it accurately
...So returning now to the immediate problem which is to calculate,or estimate,P relevance document
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