User Search Techniques
Previous chapters defined the concept of indexing and the data structures most commonly associated with Information Retrieval Systems. Chapter 5 described different weighting algorithms associated with processing tokens. Applying these algorithms creates a data structure that can be used in search. Chapter 6 describes how clustering can be used to enhance retrieval and reduce the overhead of search. Chapter 7 focuses on how search is performed. To understand the search process, it is first necessary to look at the different binding levels of the search statement entered by the user to the database being searched. The selection and ranking of items is accomplished via similarity measures that calculate the similarity between the userśs search statement and the weighted stored representation of the semantics in an item. Relevance feedback can help a user enhance search by making use of results from previous searches. This technique uses information from items judged as relevant and non-relevant to determine an expanded search statement. Chapter 6 introduces the concept of representing multiple items via an single averaged representation called a “centroid.” Searching centroids can reduce search computation, but there is an associated risk of missing relevant items because of the averaging nature of a centroid.Hyperlinked items introduce new concepts in search originating from the dynamic nature of the linkages between items cr]
KeywordsSimilarity Measure Intelligent Agent Relevance Feedback Query Term Relevant Item
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