By Shengli Wu
The means of facts fusion has been used largely in info retrieval a result of complexity and variety of projects concerned reminiscent of internet and social networks, felony, firm, etc. This publication provides either a theoretical and empirical method of facts fusion. numerous normal info fusion algorithms are mentioned, analyzed and evaluated. A reader will locate solutions to the subsequent questions, between others:
What are the foremost components that impact the functionality of knowledge fusion algorithms significantly?
What stipulations are favorable to facts fusion algorithms?
CombSum and CombMNZ, which one is best? and why?
what's the motive of utilizing the linear mix method?
How can the easiest fusion choice be stumbled on less than any given circumstances?
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Additional info for Data Fusion in Information Retrieval
Dropping terms that are the same for all documents gives a formula for calculating the log odds of relevance for ranking: n s(d) = ∑ logO[rel|ti ] i=1 Note that either rankings or scores can be used in the above formula. For better estimation, training is required for parameters setting. * * * In the above we have discussed both linear and non-linear score normalization methods. Compared with the linear combination methods, non-linear combination methods are more complicated but have the potential to achieve better results.
Five models are considered. They are Reciprocal, Borda, Informetric, Logistic, and Cubic. For the reciprocal and Borda models, estimated scores can be generated directly. , etc. For the three others, more work is required. For the informetric, logistic, and cubic models, we try to find the best suitable parameters for them. This can be done by using the observed relevance distribution to carry out regression analysis. For the cubic model, we use score as the dependent variable, and ln(rank) as the independent variable to run the curve estimation (regression)4.
For better estimation, training is required for parameters setting. * * * In the above we have discussed both linear and non-linear score normalization methods. Compared with the linear combination methods, non-linear combination methods are more complicated but have the potential to achieve better results. However, due to the diversity of score generating methods used in different information retrieval systems, special treatment for each information retrieval system is very likely required if we wish to achieve proper normalization effect.