Data Fusion in Information Retrieval by Shengli Wu

By Shengli Wu

The means of info fusion has been used greatly in details retrieval as a result of complexity and variety of projects concerned equivalent to internet and social networks, felony, firm, etc. This booklet offers either a theoretical and empirical method of info fusion. numerous usual information fusion algorithms are mentioned, analyzed and evaluated. A reader will locate solutions to the subsequent questions, between others:

What are the most important elements that have an effect on the functionality of knowledge fusion algorithms significantly?

What stipulations are favorable to information fusion algorithms?

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

what's the purpose of utilizing the linear blend method?

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

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Example text

Another option is to convert ranking information into scores. A few models have been proposed in this category. 1 Observing from the Data Without any assumption of the function that exists between rank and probability of relevance, we may simply observe that from some training data [51]. This can be done for any individual information retrieval system or a group of information 30 3 Score Normalization retrieval systems together. To make the observation reliable, a significant number of documents at each rank need to be provided and evaluated.

1 Linear Score Normalization Methods 21 next subsection) or a different data fusion method (such as the linear combination method) to improve effectiveness. , in [48] and others. The above zero-one linear method can be improved in two different ways, which will be discussed in the next two subsections. 2 The Fitting Method In many situations, [0, 1] may not be the best interval for score normalization. We consider the situation of TREC, in which each information retrieval system provides up to 1000 documents in any resultant list.

However, for the data fusion problem in information retrieval, we are in a better position to perceive which function is good and which is not. This is because in information retrieval, all documents are divided into different categories, for example, two categories, if binary relevance judgment is used. The problem of score assignment from ranking becomes to find out the relationship between probability of relevance and ranks. It is possible to find some good functions by theoretical analysis and/or empirical investigation.

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