How does Vega Discover decide search results relevancy?

In Vega Discover, for known item searches, our goal is to display the title the user entered as the first item in the search results list whenever possible. To this end, we have developed special algorithms for handling single word and exact match titles – known items – such as “It” or “Beloved” or "To Kill a Mockingbird" to insure they are visible immediately. Vega has similar algorithms for exact matches on author names with an added boost for titles published in the last two years. Vega also allows the user to search using specific known-item identifiers, such as ISBN, ISSN, or other standardized numbers, without resorting to using an advanced search. The user simply types what they want to find, and Vega Discover brings back the best matching results.  

Open-ended searching is just as easy in Vega Discover. The user can enter any terms and the relevance-based search algorithms will bring back the best matching results, regardless of whether the terms entered are an author, subject, title, serial title, or various secondary fields (editionStatement, genreForm, tableOfContents, supplementaryContent, and other note fields) or some combination of the above.  Vega also automatically applies stemming so that searches for words like 'history' will also return results for 'histories'.  Vega equates terms like it's and its, numeric punctuation like 20,000 and 20000, and hyphenation like catch-22 and catch 22 in order to help patrons find materials easily.

For open-ended searches Vega also gives users results that ONLY contain all specified terms when queries contain 3 or less terms.  Vega provides fuzzier search results when a user enters 4 or more terms, first displaying results that contain all search terms and then returning additional results that contain some but not all search terms and indicating which terms from the query are missing from each result.  You can get more exact matching for searches with 4+ terms by doing a phrase search using parenthesis or including AND between the terms. 

There are a variety of factors that affect relevancy in Vega Discover:  

• When you search for a word or phase and a title matches your query exactly, all exact match titles will be the top results. 
• When you search for an author name and an author name matches your query exactly, all books by the author will be the top results. 
• When you search for an author name and that author has published a book in the current or previous year, these books will be at the top of the results followed by other books by that author. 
• If a term is found in a highly prioritized field like Title or Author, the relevance score will be higher than if that term were found in a secondary field, such as description.  
• If a term is found multiple times within a work, then the score is higher.  
• If a term is used infrequently in the field where it was found, it is assigned a higher score.  
• If there are more matched terms in a work, a higher score will be assigned. That said, due to the previous points, a record with only one match doesn't mean automatically mean that that record will have a lower score than a record with two matches that occur in other fields. 
• When a work has many copies it is also assigned a higher score. 

Below you will find the metadata fields that we boost for search in the boosting priority order (note that the number is the same for multiple categories). This order may change as we continue to improve our relevancy based on customer feedback and user testing.