Ranking Algorithm Unveiled: How Algolia Makes Search Better (2023)

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 designedfor 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 algorithmrates 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 _rankingInfobelow 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 thatshowing 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 settingqueryType=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):

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