mnoGoSearch 3.3.9 reference manual: Full-featured search engine software
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# Ranking documents

By default, mnoGoSearch sorts results by score. Score is calculated as relevancy value mixed with various other factors listed in the Section called Commands affecting document score .

> Note: You can also request a non-default document ordering with help of the `s` search parameter. Have a look into the Section called Search parameters to know how to order documents by `Date`, `Popularity Rank`, `URL` and other document parameters.

## Commands affecting document score

Have a look into these manual sections to know about various commands that affect document ordering and/or score values: DateFactor, DocSizeWeight, MinCoordFactor, NumDistinctWordFactor, NumSections, NumWordFactor, UserScore, WordDistanceWeight, WordFormFactor, WordDensityFactor.

## Relevancy

Relevancy for every found document is calculated as the cosine of the angle formed by two weights vectors, the vector for the search query and the vector for the found document. The number of coordinates in the vectors is equal to the number of the words in the search query (`NumWords`) multiplied by the number of the active sections, defined by the NumSections command: `NumWords * NumSections`. Every coordinate in the vector corresponds to one word in one section, the coordinate value consists of thee factors:

- `section_weight`, according to the wf value for this section (see the Section called Changing weights of the different document parts at search time).

- `word_weight`, depending on whether this word is the original word from the search query typed by the user, or the word is a generated form such as a synonym or a stemming form.

  > Note: You can change the weight of the generated forms using the WordFormFactor command.

- `word_frequency` - the frequency of the word in the section, with the WordDensityFactor value taken into account.

Imagine we typed the search query ```test document`'' in the search form, and search returned this HTML document among the other results:

    <HTML>
      <HEAD>
        <TITLE>
          Test
        </TITLE>
      </HEAD>
      <BODY>
        This is a test document to test the score value
      </BODY>
    </HTML>

Also, for similicity reasons, imagine that NumSections is set to `2` (that is only the `body` and `title` sections are active), wf is set to its default value (weight factors for alls sections are equal to `1`), and WordDensityFactor is set to `255` (the strongest density effect).

mnoGoSearch will use these two vectors to calculate relevancy:

      Vq= (1, 1, 1, 1)

for the search query and

      Vd= (1, 0, 0.2, 0.1)

for the above document, calculated as follows:

- The word `test` appears once in the section `title` and its `word_frequency` is `1`, `wf[title]`=`1`, `word_weight`=`1`. Therefore, `Vd[1]`=`1 * 1 * 1` = `1`.

- The word `document` does not appear in the section `title` at all, therefore, `Vd[2]`=`0`.

- The word `test` appears two times in the section `body`, with `10` words total. `word_frequency` is `2/10`. `wf[body]` is `1`. `word_weight` is `1`. Therefore, `Vd[3]` = `2/10 * 1 * 1` = `0.2`.

- The word `document` appears once in the section `body` which is total `10` words long. `word_frequency` is `1/10`. `wf[body]` is `1`. `word_weight` is `1`. Therefore, `Vd[4]` = `1/10 * 1 * 1` = `0.1`.

The cosine value value for the above two vectors is `0.634335`.

Now imagine that we set `wf` to "`1111181`" and therefore made the weight factor for the section `title` higher. Now relevancy will be calculated using these two vectors:

      Vq= (8, 8, 1, 1)

for the search query and

      Vd= (8, 0, 0.2, 0.1)

for the above document, which will result in the relevancy value `0.704660`.

The relevancy value calculated as explained above is further mixed with various other parameters to get the final score value, for example the average distance between the words in the document, the distance of the words from the beginning of the section, and the other parameters listed in the Section called Commands affecting document score .

> Note: In the default configuration mnoGoSearch produces quite small score values, because it expects the words to be found in up to `256` sections and therefore uses the `256` coordinate vectors. Have a look into NumSections `search.htm` command description how to specify the real number of sections and thus increase the score values. Changing NumSections does not affect the document order, it only changes the absolute score values for all documents.

## Analyzing score values

Starting from the version `3.3.7`, mnoGoSearch allows to debug score values calculated for the documents found and thus helps to find a combination of all score factors which is the best for you. In order to debug score values go through these steps:

1. Add this code into the bottom of the `<!--restop-->`section of your search template:

       <--restop-->
       ....
       [DebugScore: $(DebugScore)]
       <--/restop-->

2. Add this code into the bottom of the `<!--res-->`section of your search template:

       <--res-->
       ....
       [ID=$(ID)]
       <--/res-->

3. Open search.cgiin your browser and run some search query consisting of multiple words. You will additionally see the document IDs after the usual document information.
4. Choose a document you want to see the debug information for. Remember its ID (let's say the ID is 100).
5. Go to your browser's location bar, add &DebugURLID=100at the very end of the URL and press Enter.

   > Note: Now URL will look approximately like this:

   >     http://hostname/cgi-bin/search.cgi?q=test+query&DebugURLID=100

6. Find a line of this format in between the search form and the results:

       DebugScore: url_id=100 RDsum=98 distance=84 (84/1) minmax=0.99091089
                   density=0.00196271 numword=0.90135133 wordform=0.00000000

   It will give you an idea why the score value for the selected document is too high or too low and help to fine tune various parameters like WordDistanceWeight or WordDensityFactor.

> Note: Score debug information is currently displayed only for queries with multiple search words. Queries with a single search word don't return debug information.

## Popularity rank

Popularity rank is calculated when you start `indexer -n0 -R` and is done in two steps.

At the first step, the value of the `Weight` parameter for every server is divided by the number of outgoing links from this server, so the weight of one link from this server is calculated. At the second step the sum of weights of all incoming links is calculated for every document and the result is stored as the document popularity value.

Self-links (when a document refers to itself), are ignored and do not affect the document popularity. You can also set PopRankSkipSameSite to `yes` to ignore all internal site links and thus have only inter-site links affect the popularity values.

By default, the value of the `Weight` parameter is equal to `1` for all servers. You can change this value using the ServerWeight command in `indexer.conf`.

If you put set PopRankFeedBack to `yes`, indexer will re-calculate site weights before calculating popularity values. A site weight is calculated as the sum of popularity values for all document of this site calculated during the previous `indexer -n0 -R` run. If the sum is greater than `1`, the site weight is set to this sum, otherwise, the site weight is set to `1`.

If you set PopRankUseTracking to `yes`, indexer will also use the search statistics collected using the search query tracking module (see the Section called Tracking search queries for details).

If you set PopRankUseShowCnt to `yes` in the search template file `search.htm`, the `url.shows` value (that is the value of the column `show` in the table `url`) will be incremented every time a document is displayed in search results, but only in the case when the score value of the document is greater than PopRankShowCntRatio (`25.0%` by default). That is, this option activates collecting information about the high scored documents seen by the users.

You can set PopRankUseShowCnt to `yes` in `indexer.conf`. In this case indexer will use the collected value of `url.shows` multiplied to the PopRankShowCntWeight (`0.01` by default) to the popularity value.

## Crosswords

This feature makes the words written in between the `<a href="xxx">` and `</a>` HTML tags belong to the document referenced in the link. To enable using Crosswords, use the CrossWords command in `indexer.conf` and `search.htm`.

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