Search results from the main index are displayed in a list below the Basic Search box.
The ranking of the search results uses relevancy ranking by default.
The TDNet Discover Relevancy Ranking algorithm is based on the term frequency–inverse document frequency (tf–idf) model.
The following factors contribute to the relevancy scoring and prioritization of the record in the search results.
- Term Frequency: The frequency in which a term appears in a document.
Given a search query, the higher the term frequency, the higher the document score.
- Inverse Document Frequency: The rarer a term is across all documents in the index, the higher its contribution to the score.
- Coordination Factor: The more query terms that are found in a document, the higher its score.
- Field length: The more words that a field contains, the lower it’s score.
This factor penalizes documents with longer field values.
- Field “Boosting”: Specific fields are promoted or devalued.
For example, boosting the publication date penalizes older documents.
Using this model, the most relevant and current results are delivered to the user with the highest priority regardless of publisher or provider.
An option is provided to modify the results ranking to sort by publication date from most recent to oldest records.