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In a document retrieval system where data is stored
and compared with a specific query and then compared
with other documents, we need to find the document
that is most similar to the query. The most similar
document will have the weight higher than other
documents. When more than one document are proposed
to the user, these documents have to be sorted
according to their weights. Once the result is
presented to the user by a recommender system, the
user may check any document of interest. If there are
two different documents lists, as two proposed
results presented by different recommender systems,
then, there is a need to find which list is more
efficient. To do so, the measuring tool Search
Engine Ranking Efficiency Evaluation Tool [SEREET]
came to existence. This tool assesses the efficiency
of each documents list and assigns a numerical value
to the list. The value will be closer to 100% if the
ranking list efficiency is high which means more
relevance documents exist in the list and documents
are sorted according to their relevance to the user.
and compared with a specific query and then compared
with other documents, we need to find the document
that is most similar to the query. The most similar
document will have the weight higher than other
documents. When more than one document are proposed
to the user, these documents have to be sorted
according to their weights. Once the result is
presented to the user by a recommender system, the
user may check any document of interest. If there are
two different documents lists, as two proposed
results presented by different recommender systems,
then, there is a need to find which list is more
efficient. To do so, the measuring tool Search
Engine Ranking Efficiency Evaluation Tool [SEREET]
came to existence. This tool assesses the efficiency
of each documents list and assigns a numerical value
to the list. The value will be closer to 100% if the
ranking list efficiency is high which means more
relevance documents exist in the list and documents
are sorted according to their relevance to the user.