Ranking Algorithm Unveiled: How Algolia Makes Search Better

Most search engines rank results based on a unique float value that is hard, if not impossible, to decipher. This is because their ranking algorithm has been designed for document search. They take into account the number of occurrences of query words in matching documents to determine their relevance, usually using a tf-idf based scoring.

We designed Algolia with database search as our main use-case. The foremost impact of this design decision is that we don’t care about the number of occurrences of query words. Instead of assigning a global float score to each result, our ranking algorithm rates each matching record on several criteria (such as the number of typos or the geo-distance), to which we individually assign a integer value score.

You even have the option to assign a custom criterium, allowing you to consider additional factors for your ranking, instead of applying a superficial boost has nothing to do with the ranking and that can seriously muddy your results.

Here is how it works:

  • All the matching records are sorted according to the first criterion.
  • If any records are tied, those records are then sorted according the second criterion.
  • If there are still records that are tied, those are then sorted according to the third criterion
  • and so on, until each record in the search results has a distinct position.

A record’s score on each criterium is explicitly listed in the search results (see _rankingInfo  below for the query “the rains”), so you can understand why one record ranked higher than another one. We will explain each of these criteria in this article.

{"hits": [
{
    "name": "The Rains Came",
    "url": "/title/tt0031835/",
    "rating": 6.8,
    "year": "(1939)",
    "nb_voters": 881,
    "rank": 16232,
    "objectID": "24324",
    "_highlightResult": {
        "name": {
            "value": "<em>The</em> <em>Rains</em> Came",
            "matchLevel": "full"
        },
        "year": {
            "value": "(1939)",
            "matchLevel": "none"
        }
    },
    "_rankingInfo": {
        "nbTypos": 0,
        "firstMatchedWord": 0,
        "proximityDistance": 1,
        "userScore": 2379657,
        "geoDistance": 0,
        "geoPrecision": 1,
        "nbExactWords": 2
    }
},
...
}

Search-as-you-type

Before diving into our “secret sauce”, you first need to understand that Algolia searches for matching prefixes, not matching whole-words. For example, if you are searching for “Joe B”, we would consider all the following records as matches:

  • Joe Black
  • Joe Benson
  • Joe Bolick

Prefix matching is what enables us to return relevant results even when a user has only typed a single letter. When Google introduced instant search, they claimed that showing results before you finish typing can save 2-5 seconds per search.

Note: By default, when the query contains multiple terms, Algolia only uses the last term as a prefix. This is because when searching, say, for a person by name, it’s quite normal to type their entire first name but not their last (e.g George Cloo). Not so for the reverse (e.g. Geo Clooney). You can override this behavior by setting  queryType=prefixAll .

Ranking algorithm criteria

By default, Algolia ranks every matching record by using the following criteria, in the order listed below. The higher up the criterion on the list, the more importance it has on ranking. You can easily change this order if you want, but we have found that this default order is the best one in 90% of the use cases.

  1. Typos
  2. Geo-location (if applicable)
  3. Proximity
  4. Attributes
  5. Exact
  6. Custom

Let’s understand each one of these criteria by applying them to an example:

[
  {
    "objectID": 1,
    "name": "Jo Blak",
    "company": "Utility Trailer Sales",
    "nbCalls": 4
  },
  {
    "objectID": 2,
    "name": "Jo T. Black",
    "company": "Steritek Inc",
    "nbCalls": 45
  },
  {
    "objectID": 3,
    "name": "Joe Black",
    "company": "Pip Printing",
    "nbCalls": 9
  },
  {
    "objectID": 4,
    "name": "Joe Thompson",
    "company": "Black Birds inc",
    "nbCalls": 9
  },
  {
    "objectID": 5,
    "name": "Deanna Gerbi",
    "company": "Thompson, Joey & Blackburn ltd",
    "nbCalls": 7
  }
]

1. Typos

Are there words that start (that is, are prefixed) with a term typed by the user? And if so, do they match exactly the query?

  • 0 points means there are prefixes that exactly match all the terms in the query.
  • 1 point means there is a 1-character discrepancy between the matching prefixes and the query terms.
  • 2 points means there is a 2-character discrepancy, and so on.

Example: for the query “joe black”, here is how each result would rank for typos only (joey is considered as a typo as only the last word of the query is searched as a prefix):

Rank

Record

Score

Why

1

record 3

0

joe black

1

record 4

0

joe thompson black birds inc 

1

record 5

1

thompson, joe_ & blackburn ltd

2

record 2

1

jo_ t. black

3

record 1

2

jo_ bla_k

Note: By default, Algolia accepts 1 typo for words having at least 3 characters and 2 typos for words having at least 7 characters (this behavior can be configured withminWordSizefor1Typo  andminWordSizefor2Typos  query parameters). This means that the query “ab” only matches words starting with “ab”, while the query “abc” matches words starting by “abc” but also “aba”, “abb”, “aac”, etc.  A typo is defined by an insertion, deletion, or substitution of a single character, or a transposition of two adjacent characters (Damerau–Levenshtein distance). As it is extremely unusual to mistype the first character of a word, a typo on the first character counts for 2 points instead of 1.

2. Geo-location (if using)

Is the record found within a certain radius of the specified location? And if so, how far from it? The geoDistance  score is expressed in meters, the shorter the better.

However, you may want to consider results “100m distant” and “102m distant” equal for ranking consideration. To do so, you can use the aroundPrecision  query parameter. For example, with aroundPrecision=10 , two results up to 10 meters close will be considered equal.

We don’t use geo-location in our example, but you can find a dedicated guide in our documentation.

3. Proximity

For a query that contains two or more words, how physically near are those words in the matching record?

Algolia adds 1 point for each word in between query words, with a maximum of 8 points.

  • 0 points means no proximity: there was only one word in the query.
  • 1 point means the best possible match: the words are next to each other.
  • 2 points means there is one word between the matched query words.
  • and so on.

When words are in different attributes they get automatically the maximum of 8 points per new attribute. So if three query words are in three different attributes, the score is 16. If three words are in two different attributes, the score is 8.

In our example, we have a 3-way tie between records 1, 3 and 5 (‘&’ is considered as a separator and is not taken into account). Record 2 has a word in between the matched query words (Jo T. Black), while record 4 matches in two different attributes:

Rank

Record

Score

1

record 1

1

1

record 3

1

1

record 5

1

1

record 2

2

2

record 4

8

4. Attributes

This is the order of the attributes (fields) Algolia will follow to search inside a record. Records where there is a match in the 1st listed attribute rank higher (that is, gets fewer points) than records with a match in an attribute that’s lower on the list.

Depending of the first matching attribute, results will get a score in a specific range:

  • 1st attribute: 0-999 points
  • 2nd attribute: 1000-1999 points
  • 3rd attribute: 2000-2999 points
  • and so on.

The exact number of points are determined by the position of the first matching word in the attribute:

  • 1st word in attribute: 0 
  • 2nd word in attribute: 1
  • 3rd workd in attribute: 2

In our example, say we consider the name as more important than the company. We would then use the setting attributesToIndex:[“name”, “company”] to indicate that we want to index, i.e. search in, the attributes “name” then “company”, in this specific order of importance.

Rank

Record

Score

1

record 1

0

1

record 2

0

1

record 3

0

1

record 4

0

2

record 5

1001

Lastly, matching text at the beginning of a given attribute will be considered more important than matching text further in this attribute. You can disable this behavior if you add your attribute inside unordered(AttributeName). If we considered the position of the match not relevant for the attribute “company”, we would use the setting attributesToIndex:[“name”, “unordered(company)”] . In that case the “attribute score” of record 5 would be 1000 and not 1001.

5. Exact

Records with words (not just prefixes) that exactly match the query terms rank higher. A record gets 1 point for every word that is exactly matched.

Here is how our records would rank based on exact-matching alone for the query “joe black”:

Rank

Record

Score

Why

1

record 3

2

joe black

1

record 4

2

joe tompson black bird inc

2

record 2

1

jo t. black

3

record 1

0

3

record 5

0

 

6. Custom / Business metrics

At this stage, the previous five criteria have ascertained a record’s relevance for a user’s search query. Now you can specify additional criteria.

A common approach is to use one or several business metrics that express the popularity of a record. With other search engines, you have to choose between sorting the results according to their relevance to the user’s query, or according to their popularity (number of visits, ratings, sales, etc). You just cannot do both. This means users may get results that are outrageously popular, but completely irrelevant to their search.

With Algolia, you can integrate popularity (or anything else, like population, or the last date of update) into the relevance calculation. To us, it is just an additional criterium so it will not outweigh classic relevance criteria. The ranking will just make additional sense.

In our example, we may consider people with whom we had many calls more popular than others. For people having the same number of calls, we can just order them by alphabetical order. We would then use the setting:  customRanking:[“desc(nbCalls)”, “asc(name)”]

For this criterium alone, here’s how our example records rank:

Rank

Record

Score

Why

1

record 2

4

nbCalls=45

2

record 3

3

nbCalls=9, “Joe B”  < “Joe T”

3

record 4

2

nbCalls=9

4

record 5

1

nbCalls=7

5

record 1

0

nbCalls=4

The score is actually the order of entries in the index (biggest score being first). There is never equal scores for this criterium. Therefore, custom should always be the last criterium of your ranking as no subsequent criterium would ever be checked.

Note: Custom ranking is computed at index time (for performance reasons) and cannot be changed dynamically with each query. If you need to change the ranking depending on context, you need to create one index per desired ranking formula. Algolia proposes a primary/replica feature to ease the task of keeping several indices in sync. You only need to push your updates to the primary and they are automatically replicated to the replica indices (see the replica  parameter in index settings).

Determining the overall rank

Let us know determine the exact ranking of our query “joe black”.

1. Typos: After looking at typos, we can already rank record 1 as last. Since record 3 and 4, as well as record 2 and 5, are tied, we need to compare them to the next criterion.

Typos

Record 3

0

Record 4

0

Record 2

1

Record 5

1

Record 1

2

2. Geo:  Not applicable. All records have a score of 0. Next!

Typos

Geo-distance

Record 3

0

0

Record 4

0

0

Record 2

1

0

Record 5

1

0

Record 1

2

3. Proximity: Record 4 matches in two distinct attributes is thus scored less that record 3. Record 2 has a word (T.) between query terms and thus scores less than record 5.

Typos

Geo-distance

Proximity

Record 3

0

0

1

Record 4

0

0

8

Record 5

1

0

1

Record 2

1

0

2

Record 1

2

Since each record now has a distinct rank, in this example there’s no need to go through another round and compare scores for the Attributes, Exact and Custom criteria.

A second example

Before jumping to our conclusion, let’s now look at what would be the result for the query composed of the single character ‘j’:

Typo

Geo-distance

Proximity

Attributes

Exact

Custom

Record 2

0

0

0

0

0

4

Record 3

0

0

0

0

0

3

Record 4

0

0

0

0

0

2

Record 1

0

0

0

0

0

1

Record 5

0

0

0

1001

With such a simple query, we obtain a 4-way tie before checking the custom score that will finally consider record 2 as the best one because of the important number of calls it received.

Conclusion

The advantage of prefix matching in our ranking algorithm is that Algolia initially casts a wide net of results that are already relevant and literally gets closer to the mark as you type each additional letter. Again, these are the results you get out of the box.

You can easily configure ranking calculations by:

  • changing criteria order (see ranking  in the settings)
  • changing the order of the attributes (see attributesToIndex  in the settings)
  • defining a custom criterion (see customRanking  in the settings)
  • changing your geographic precision (see aroundPrecision  in the search parameters)
  • vivekananthan

    This is a real beauty 🙂 Definitely a well thought out for searching user objects 🙂 Well done Team