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Data processing system and method

A data processing system and data processing technology, applied in data processing applications, electrical digital data processing, special data processing applications, etc.

Inactive Publication Date: 2004-10-13
UNIV COLLEGE DUBLIN NAT UNIV OF IRELAND DUBLIN
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, if the multiple recommendations are related (similar to the target query) and diverse, there is a greater chance of success

Method used

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  • Data processing system and method
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Examples

Experimental program
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Embodiment Construction

[0024] In an example-based recommender, for example c and the target query t A common way to measure similarity between is to use a weighted sum metric, Equation 1.

[0025] ...equation 1

[0026] In this weighted sum metric, the target t and example c Each has n features that can be compared using the similarity function sim, each feature has a weight w attached to it. For example, if the search is for a 14-night planned holiday at a cost of $800, preferably within 20 kilometers of the airport, there will be three features for comparison, and the function sim can be calculated as follows:

[0027] (i=1)

[0028] Number of nights: sim(t 1 , c 1 )=1 if|t 1 -c 1 |≤1

[0029] = 0 else

[0030] (So ​​when the target query specifies 14 nights, the sim function returns a value of 1 for cases with a duration of 13, 14, or 15 nights,)

[0031] (i=2)

[0032] Price: sim(t 2 , c 2 )=1-(|t 2 -c 2 | / max(t 2 c 2 ))

[0033] where|t 2 -c 2 | i...

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Abstract

A data processing method and system for retrieving a subset of k items from a database of n items (n>=k) firstly determines a limited set of bk items (b>1) in the database which have the greatest similarity to an input query t according to a given similarity function S. A result subset is then constructed by including as a first member the item having the greatest similarity S to the query t, and iteratively selecting each successive member of the subset as that remaining item of the bk items having the highest quality Q, where Q is a given function of both similarity to the input query t and relative diversity RD with respect to the items already in the results subset. In this way the diversity of the results subset is greatly increased relative to a simple selection of the k most similar items to the query t, with only a modest additional increase in processing requirements.

Description

technical field [0001] The present invention relates to a system and method for retrieving data from a database according to predetermined criteria. Background technique [0002] A case-based reasoning (CBR) system solves new problems by reusing solutions to problems that have previously been solved as cases stored in a case repository. The success of any CBR system relies on its ability to select the correct paradigm for the correct target problem (see e.g. Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, 1993; Smyth, B. and Keane, M.: Adaptation-Guided Retrieval: Questioning the Similarity Assumption in Reasoning. Artificial Intelligence, 102: 249-293, 1998; Aamodt, A. And Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, 7(1) : 39-52, 1994). [0003] The conventional wisdom is that the correct paradigms are among those most similar to the target problem, so considerable research effort has been...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCY10S707/99936Y10S707/99935Y10S707/99939Y10S707/99933G06F17/30533Y10S707/99932Y10S707/99934Y10S707/99937Y10S707/99938Y10S707/99942G06F16/2458
Inventor 巴里·J·史密斯
Owner UNIV COLLEGE DUBLIN NAT UNIV OF IRELAND DUBLIN