Relevance is a measurement of the user's satisfaction with the response to their search query. It completely depends on the context of the search. Sometimes, the same document can be searched by different classes of people for different context. For example, the search query higher tax payer in India can be searched by:

• An income tax department in the context of their duty
• Chartered accountants in the context of their professional interest
• Students in the context of gaining knowledge

The comprehensiveness of any response depends on the context of the search. Sometimes, the context is high, such as searching for legal information; sometimes, it is low, when someone is searching for context such as specific dance steps. So, during Solr configuration, we need to take care of this too.

There are two terms that play an important role in relevance:

• Precision: Precision is the percentage of documents in the returned results that are relevant.
• Recall: Recall is the percentage of relevant results returned out of all relevant results in the system. Retrieving perfect recall is insignificant, for example, returning every document for every query.

From this example, we can conclude that precision and recall totally depend on the context of the search. Sometimes, we need 100% recall, say when searching for legal information. Here, all the relevant documents should be returned in the response. While in other scenarios, there is no need to return all documents. For example, when searching for dance steps, returning all the documents will overwhelm the application.

Through faceting, query filters, and other search components, the application can be configured with the flexibility to help end users get their searches, in order to return the most relevant results for users. We can configure Solr to balance precision and recall to meet the needs of a particular user community.