A good search engine tries to answer the underlying question
But only by analyzing the on-site and off-site factors is it possible for Google to determine which pages will answer is the question behind the query. For this Google will have to analyze the text on a page.
True OR False
Search engines have evolved tremendously in recent years, but at first they could only deal with Boolean operators. In simple terms, a term was included in a document or not. Something was true or false, 1 or 0. Additionally you could use the operators as AND, OR and NOT to search documents that contain multiple terms or to exclude terms.
Search engines have evolved tremendously in recent years, but at first they could only deal with Boolean operators. In simple terms, a term was included in a document or not. Something was true or false, 1 or 0. Additionally you could use the operators as AND, OR and NOT to search documents that contain multiple terms or to exclude terms.
Number of times you use a keyword term is not necessarily important. It is important to find the right balance for the keyword terms you want to rank.
An explanation of the table below:
tf = term frequency
df = document frequency
idf = inverse document frequency
Wt,q = weight for term in query
Wt,d = weight for term in document
Product = Wt,q * Wt,d
Score = Sum of the products
formula for this is as follows:
The table below is a visual representation of this formula. Suppose we apply the following values :
Query terms: +1 (alpha)
Relevant terms: +1 (beta)
Irrelevant terms: -0.5 (gamma)
The table below is a visual representation of this formula. Suppose we apply the following values :
Query terms: +1 (alpha)
Relevant terms: +1 (beta)
Irrelevant terms: -0.5 (gamma)
Speed up the process
static values to determine for which documents you want to calculate the score. For example PageRank is a good static value. When you first calculate the score for the pages matching the query and having an high PageRank, you have a good change to find some documents which would end up in the top 10 of the results anyway.
static values to determine for which documents you want to calculate the score. For example PageRank is a good static value. When you first calculate the score for the pages matching the query and having an high PageRank, you have a good change to find some documents which would end up in the top 10 of the results anyway.
Relevance feedback
Relevance feedback is assigning more or less value to a term in a query, based on the relevance of a document. Their first attempt was by adding the favorite star at the search results. Now they are trying it with the Google+ button. If enough people start pushing the button at a certain result, Google will start considering the document relevant for that query.
Relevance feedback is assigning more or less value to a term in a query, based on the relevance of a document. Their first attempt was by adding the favorite star at the search results. Now they are trying it with the Google+ button. If enough people start pushing the button at a certain result, Google will start considering the document relevant for that query.
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