In Elasticsearch, the relevance score is often calculated using the formula: score = tf * idf, where tf is term frequency and idf is inverse document frequency.
If a document has a term frequency of 3 and the inverse document frequency of the term is 2, what is the relevance score?
Multiply term frequency by inverse document frequency.
The relevance score is calculated as tf * idf. Here, 3 * 2 = 6.
Which of the following best explains why Elasticsearch uses inverse document frequency (IDF) in its relevance scoring?
Think about how rare terms affect relevance.
IDF gives more weight to rare terms because they are more informative for distinguishing documents.
Elasticsearch applies field length normalization to reduce the score of longer fields. Given the formula:
score = (tf * idf) / sqrt(field_length)
If tf = 4, idf = 3, and field_length = 16, what is the score?
Calculate the square root of the field length first.
Square root of 16 is 4. So score = (4 * 3) / 4 = 3.
Consider this Elasticsearch query snippet:
{
"query": {
"match": {
"content": {
"query": "apple banana",
"operator": "and"
}
}
}
}All documents have zero relevance scores. What is the most likely reason?
Think about how the 'and' operator affects matching.
The 'and' operator requires all terms to be present. Documents missing any term get zero score.
When a query contains multiple terms, Elasticsearch combines the individual term scores to produce a final relevance score for a document. Which method does Elasticsearch use by default to combine these scores?
Think about how Elasticsearch rewards documents matching more terms.
Elasticsearch sums the scores of all matching terms to favor documents that match more query terms.