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# Tender Miner

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### Tender Miner

1. 1. Tender Analysis Module<br />Paras Deshpande<br />
2. 2. Tenders<br />The tenders have various attributes like country, document type, CPV codes.<br />These attributes can have different values depending upon the particular tender in question.<br />Also the tender can have same attribute with different values e.g. CPV Codes.<br />
3. 3. Methodology: Analysis of Tenders<br />The attributes of the tenders have been formulated as Categories. These are therefore general representation of the attributes.<br />The various specific values that the attributes takes are captured into criteria. The tender thus has a dual level one with a broader scope while the other encapsulates the specific information.<br />
4. 4. Methodology: Analysis of Tenders cont<br />The more categories a tender has the more attributes it will be evaluated upon.<br />The more criteria&apos;s a category has the category will become more comprehensive and general.<br />The ranking given to a criteria is normalised over other criteria within the category.<br />
5. 5. Advantages of Weighted Criteria Algorithm<br /> The Algorithm offers various advantages enumerated below:<br />Firstly, the Algorithm is simple and intuitive yet very effective in case of Tender Analysis.<br />The Algorithm is scalable and can be robustly implemented for very large number of Tenders.<br />
6. 6. AHP vis-à-vis Weighted Criteria <br />AHP is effective when decisions involve many intangibles to be measured along side tangibles whose measurements must also be evaluated by the decision maker <br />Therefore when the measurement of the criteria’s assumes subjectivity then AHP is the method of choice.<br />In case of Tender Analysis the measurement of the criteria’s is tangible and hence the Weighted Criteria method preferred apart from the scalability issues.<br />
7. 7. Mathematical formulation <br />
8. 8. Notations<br />
9. 9. Weightages<br /><ul><li>We then Normalise the weightages to the scale of -1 to +1</li></li></ul><li>Calculation of Tender Score<br />
10. 10. i<br />Preference<br />
11. 11. Need for iPreference <br />iPreference stands for inferred preferences.<br />iPreference is the logical extension of the Tender Miner because of its unique capability to infer, map and store user preferences.<br />
12. 12. Features of iPreference<br />User can now store previously won tenders as favorites which will subsequently be used to infer their preferences and hence rank tenders according to inferred preferences. <br />This will greatly improve the user experience by saving him from cumbersome task of filling the forms and also address changing preferences.<br />
13. 13. Methodology<br />The User will be required to fill in the categories he wishes to have the tenders judged upon along with their weightages.<br />He then has a option of either judging some tenders provided by the service OR can add a list of previously won tenders to his favorites.<br />Now the iPreference uses these tenders to infer<br /> user preference and adds his inferred profile to the knowledge Base.<br />
14. 14. Methodology cont<br />The logic used for inferring is described next:<br />Ranking Tenders requires categories and their weightages, criteria and their weightages.<br />In this case instead of criteria we have tenders with their weightages.<br />Therefore a simple correlation logic would suggest: More favorable a tender is More favorable its criteria&apos;s are expected to be.<br />
15. 15. Advantages<br />This is the first* time a reverse approach is being taken for decision making.<br />Its reverse in a sense it presents the reference alternatives and then ranks candidate alternatives on the basis of knowledge gained rather than taking down the preferences first.<br />* To best of my knowledge<br />
16. 16. Finer Aspects<br />There are some issues which need further attention. For e.g. if a favorable tender might have some favorable and some unfavorable criteria&apos;s, and presently iPreference cannot differentiate between them. <br />It may be agued though as more tenders and fairly representative tenders are added to the list the unfavorable criteria’s will slowly get neglected as happens with heuristics. <br />Eventually the better criteria will prevail !!! <br />
17. 17. Finer Aspects cont<br />This method is similar to Neural Networks in many cases and has similar advantages and limitations.<br />This method requires less effort on learning routines.<br />Under learnt profile might result in substandard results and a minimum of 10-15 tenders should be included in favorites. <br />
18. 18. Mathematical Formulations<br />
19. 19. Notations<br />
20. 20. Weightages<br /><ul><li> Normalized weightages : </li></li></ul><li>Algorithm<br />
21. 21. Ending Remarks<br />The Algorithm shows that with different types of tenders being added to the favorites the favorable criteria will occur more frequently and thus grow stronger in weightages.<br />There can be improvements on finding ways to promote favorable criteria&apos;s and eliminating the unfavorable ones.<br />The main contribution thus will be proposing a intuitive and simple inference mechanism. <br />
22. 22. References<br />http://people.revoledu.com/kardi/tutorial/AHP/Multi-Criteria-Decision-Making.htm<br />Saaty, T.L. (2008) ‘Decision making with the analytic hierarchy process’, Int. J. Services Sciences, Vol. 1, No. 1, pp.83–98.<br />
23. 23. End of Slides<br />