By Shengli Wu

ISBN-10: 3642288650

ISBN-13: 9783642288654

ISBN-10: 3642288669

ISBN-13: 9783642288661

The means of info fusion has been used largely in info retrieval end result of the complexity and variety of projects concerned resembling net and social networks, criminal, firm, etc. This e-book provides either a theoretical and empirical method of facts fusion. a number of common info fusion algorithms are mentioned, analyzed and evaluated. A reader will locate solutions to the next questions, between others:

What are the foremost elements that impact the functionality of information fusion algorithms significantly?

What stipulations are favorable to information fusion algorithms?

CombSum and CombMNZ, which one is best? and why?

what's the reason of utilizing the linear mix method?

How can the simplest fusion alternative be came across lower than any given circumstances?

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Extra resources for Data Fusion in Information Retrieval

Example text

For zero-one, sum-to-one and Z1, we observe that very often their curves fly into the sky in Intervals 1 and 2. This is understandable since all the normalized scores with zero-one and sum-to-one are approaching 0 in Intervals 1 and 2. It is not the case for their raw scores and there are a few relevant documents in these two intervals. The curves are deformed because the corresponding scores are undervalued considerably. The situation is even worse for Z1 since negative and positive scores coexist in Interval 1.

4, we have discussed a range of score normalization methods. Some methods are more sophisticated than others. Though under the title of “score normalization”, some sophisticated score normalization methods do have a component of weighting assignment for different information retrieval systems. This is because in some cases, system performance is taken into consideration, but similarity between systems has been touched by none of them. Some of the methods such as the zero-one linear method, global scores, Z-scores, sum-to-one, the Borda model, and the reciprocal model do not consider system performance.

Such a difference makes the logistic model very good while the informetric model not very good. Note that for data fusion, the accuracy of scores for those top-ranked documents is more important than other parts of a result. The logistic model performs as good as the cubic model in TREC 9, but it is not as good as the cubic model in TREC 8. In the latter case, the difference between them is not large. It is notable that for all those models, their performances in both groups are consistent. It also suggests that the results are very reliable about the comparison of them.

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