By Shengli Wu
The means of info fusion has been used widely in info retrieval end result of the complexity and variety of initiatives concerned akin to internet and social networks, criminal, firm, etc. This publication offers either a theoretical and empirical method of information fusion. numerous common info fusion algorithms are mentioned, analyzed and evaluated. A reader will locate solutions to the next questions, between others:
What are the most important components that impact the functionality of information fusion algorithms significantly?
What stipulations are favorable to info fusion algorithms?
CombSum and CombMNZ, which one is best? and why?
what's the cause of utilizing the linear mix method?
How can the simplest fusion alternative be discovered less than any given circumstances?
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Additional resources for Data Fusion in Information Retrieval
8) Pr (t) = i=t−u 2u + 1 Here 2u + 1 is the size of the sliding window. Apart from ranking position t, u ranking positions on both left and right sides are considered as well. At some very top ranks and very bottom ranks, fewer neighbouring positions are used if not all required exist. Another possible solution is that sliding windows may be defined to be of variable size. 2 The Borda Count Model In the Borda count, documents are given scores based on their ranks. , and the last one is given 1 point.
12, if we let c = 1 and b = 1, then we obtain the reciprocal function of rank. 12. 4. 12. 13 is small, when t is large (1 << e−a ∗ t −b ). But they are quite different when t is small. 15 can be used to find parameters ln(c) and b by linear regression with some training data which provide observed relevance distribution. See next subsection for more related discussion. It seems that there is no relation between the modified cubic model and the informetric distribution. However, we should bear in mind that any functions can be estimated by a polynomial, especially with large degree terms.
Next let us see some preliminary findings about this. Two groups of TREC results (9 and 2001 web track) are used. 6. As a matter of fact, both of them use the 3-graded relevance judgment. That is to say, any documents are divided into three categories: highly relevant, modestly relevant, and irrelevant. 6, both modestly relevant and highly relevant documents are treated equally as relevant documents. Thus 3-graded relevance judgment becomes binary relevance judgment. Here we use the original 3-relevance judgment to investigate the score normalization issue.