[OPEN-ILS-GENERAL] question about relevance - mult eds of same book not in most recent order

[Karen Schneider]

Indexed-field weighting, which controls relevance ranking in Evergreen, is
configured in the database (no UI available yet) on the table called
config.metabib_field, using the ‘weight’ column.

(The other four columns are field_class, name, xpath, and format; the table
is too wide to display in this email, but here is one line: author    |
conference  |
//mods32:mods/mods32:name[@type='conference']/mods32:namePart[../mods32:role/mods32:roleTerm[text()='creator']]
| mods32 |      1 )

The default value for index-field weights is “1.” Adjust the weighting of
indexed fields to give those fields a boost in searching. The larger the
value for ‘weight,' the higher the relevance score for matches on that
indexed field.

For example, by increasing the weight of the title-proper field,  a search
for *jaguar* would give higher relevance to the book titled *Aimee and
Jaguar *than to a record with the term *jaguar *in another indexed field.

You can also add generic matchpoint bonuses for the following types:

*first_word* — boosts relevance if the query is one term long and matches
the first term in the indexed field (search for *twain*, get a bonus
for *twain,
mark* but not* mark twain*)

*word_order* — increases relevance for words matching the order of search
terms, so that the results for the search *legend suicide* would match
higher for the book *Legend of a Suicide* than for the book, *Suicide Legend
*

*full_match* — full_match — boosts relevance when the full query exactly
matches the entire indexed field (after space, case and diacritic
normalization on both). So a title search for *The Future of Ice* would get
a relevance boost above *Ice Ages of the Future*.  **

The matchpoint bonuses are configured on a table called
search.relevance_adjustment, using the ‘multiplier’ column.  That is a
floating-point multiplier, where the relevance score is multiplied by that
at the end.  So, if the first-word bonus is 1.2, then the relevance score
gets a 20% bonus (x * 1.2).

The search.relevance_adjustment weighting can be adjusted for each field.

The search.relevance_adjustment table has three other columns: field_class,
name, and bump_type. Here are several lines from the
search.relevance_adjustment table:
 title       | translated  | word_order |         10
title       | uniform     | first_word |        1.5
title       | uniform     | full_match |         20
title       | uniform     | word_order |         10

Does that help? If so, I'll put this on the DocBook docket.

Big ol' thanks to Mike Rylander for helping me with this answer!

-- 
-- 
| Karen G. Schneider
| Community Librarian
| Equinox Software Inc. "The Evergreen Experts"
| Toll-free: 1.877.Open.ILS (1.877.673.6457) x712
| kgs at esilibrary.com
| Web: http://www.esilibrary.com
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