MarkLogic Cookbook

Chapter 8. Scoring Search Results

MarkLogic is a database that contains a powerful search engine. There are advantages to this, such as the fact that data does not need to be replicated to a search engine to provide that functionality, search results are up to date as soon as a transaction completes, and the search is subject to the same security as the database content.

While running a search, MarkLogic assigns a score that accounts for the frequency of your target terms within the database, the frequency of the terms within each document, and the length of the document. For a detailed explanation of how scores are calculated, see “Understanding How Scores and Relevance are Calculated” in the Search Developer’s Guide.

The recipes in this chapter show some tricks to affect the way search results are scored.

Sort Results to Promote Recent Documents

Problem

Show more recent documents higher in a result set than older documents. For instance, when searching blog posts, more recent content is more likely to be current and relevant than older content.

Solution

Applies to MarkLogic versions 8 and higher

With server-side code:

var jsearch = require('/MarkLogic/jsearch.sjs');
jsearch.documents()
  .where([
    cts.elementRangeQuery(
      fn.QName("", "pubdate"), "<=", fn.currentDateTime(),
      "score-function=reciprocal")
  ])
  .result()

With the MarkLogic REST API:

{
  "search": {
    "qtext": "recent LE " + fn.currentDateTime()
    "options": {
      "constraint": [
        {
          "name": "recent",
          "range": {
            "facet": false ...