How to boost your search results with filter scoring
In this guide, you’ll discover how to implement a filter scoring feature to enhance your search functionality within Meilisearch.
What is filter boosting?
Filter boosting, also referred to as filter scoring, is an advanced search optimization strategy designed to enhance the relevance and precision of returned documents. Instead of simply returning documents that match a single filter, this method uses a weighted system for multiple filters. The documents that align with the most filters—or those that match the most heavily-weighted filters—are given priority and returned at the top of the search results.
Generating filter boosted queries
Meilisearch allows users to refine their search queries by adding filters. Traditionally, only documents that precisely match these filters are returned in the search results.
With the implementation of filter boosting, you can optimize the document retrieval process by ranking documents based on the relevancy of multiple, weighted filters. This ensures a more tailored and effective search experience.
The idea behind this implementation is to associate a weight to each filter. The higher the value, the more important the filter should be. In this section, we’ll demonstrate how to implement a search algorithm that makes use of these weighted filters.
Step 1 — Setting up and prioritizing filters: weights assignment
To leverage the filter scoring feature, you’ll need to provide a list of filters along with their respective weights. This helps prioritize the search results according to the criteria that matter most to you.
Example input using JavaScript:
const filtersWeights = [
{ filter: "genres = Animation", weight: 3 },
{ filter: "genres = Family", weight: 1 },
{ filter: "release_date > 1609510226", weight: 10 }
]In the example above:
- The highest weight is assigned to the release date, indicating a preference for movies released after 2021
- Movies in the “Animation” genre get the next level of preference
- “Family” genre movies also receive a minor boost
Step 2. Combining filters
The goal is to create a list of all filter combinations, where each combination would be associated with its total weight.
Taking the previous example as a reference, the generated queries with their total weights would be as follows:
("genres = Animation AND genres = Family AND release_date > 1609510226", 14)
("genres = Animation AND NOT(genres = Family) AND release_date > 1609510226", 13)
("NOT(genres = Animation) AND genres = Family AND release_date > 1609510226", 11)
("NOT(genres = Animation) AND NOT(genres = Family) AND release_date > 1609510226", 10)
("genres = Animation AND genres = Family AND NOT(release_date > 1609510226)", 4)
("genres = Animation AND NOT(genres = Family) AND NOT(release_date > 1609510226)", 3)
("NOT(genres = Animation) AND genres = Family AND NOT(release_date > 1609510226)", 1)
("NOT(genres = Animation) AND NOT(genres = Family) AND NOT(release_date > 1609510226)", 0)We can see, when filters match Criteria 1 + Criteria 2 + Criteria 3, the total weight is weight1 + weight2 + weight3 ( 3 + 1 +10 = 14).
Below, we'll explain how to build this list. For details on automating this process, refer to the Filter combination algorithm section.
Then, you can use Meilisearch’s multi-search API to perform queries based on these filters, arranging them in descending order according to their assigned weight.
Step 3. Using Meilisearch’s multi-search API
npm install meilisearch
\\ or
yarn add meilisearch
const { MeiliSearch } = require('meilisearch')
// Or if you are in a ES environment
import { MeiliSearch } from 'meilisearch'
;(async () => {
// Setup Meilisearch client
const client = new MeiliSearch({
host: 'http://localhost:7700',
apiKey: 'apiKey',
})
const INDEX = "movies"
const limit = 20
const queries = [
{ indexUid: INDEX, limit: limit, filter: 'genres = Animation AND genres = Family AND release_date > 1609510226' },
{ indexUid: INDEX, limit: limit, filter: 'genres = Animation AND NOT(genres = Family) AND release_date > 1609510226' },
{ indexUid: INDEX, limit: limit, filter: 'NOT(genres = Animation) AND genres = Family AND release_date > 1609510226' },
{ indexUid: INDEX, limit: limit, filter: 'NOT(genres = Animation) AND NOT(genres = Family) AND release_date > 1609510226' },
{ indexUid: INDEX, limit: limit, filter: 'genres = Animation AND genres = Family AND NOT(release_date > 1609510226)' },
{ indexUid: INDEX, limit: limit, filter: 'genres = Animation AND NOT(genres = Family) AND NOT(release_date > 1609510226)' },
{ indexUid: INDEX, limit: limit, filter: 'NOT(genres = Animation) AND genres = Family AND NOT(release_date > 1609510226)' },
{ indexUid: INDEX, limit: limit, filter: 'NOT(genres = Animation) AND NOT(genres = Family) AND NOT(release_date > 1609510226)' }
]
try {
const results = await client.multiSearch({ queries });
displayResults(results);
} catch (error) {
console.error("Error while fetching search results:", error);
}
function displayResults(data) {
let i = 0;
console.log("=== best filter ===");
for (const resultsPerIndex of data.results) {
for (const result of resultsPerIndex.hits) {
if (i >= limit) {
break;
}
console.log(`${i.toString().padStart(3, '0')}: ${result.title}`);
i++;
}
console.log("=== changing filter ===");
}
}
})();We begin by importing the required libraries for our task. Then we initialize the Meilisearch client, which connects to our Meilisearch server, and defines the movie index we’ll be searching.
Next, we send our search criteria to the Meilisearch server and retrieve the results. The multiSearch function lets us send multiple search queries at once, which can be more efficient than sending them one by one.
Finally, we print out the results in a formatted manner. The outer loop iterates through the results of each filter. The inner loop iterates through the hits (actual search results) for a given filter. We print each movie title with a number prefix.
We get the following output:
=== best filter ===
000: Blazing Samurai
001: Minions: The Rise of Gru
002: Sing 2
003: The Boss Baby: Family Business
=== changing filter ===
004: Evangelion: 3.0+1.0 Thrice Upon a Time
005: Vivo
=== changing filter ===
006: Space Jam: A New Legacy
007: Jungle Cruise
=== changing filter ===
008: Avatar 2
009: The Flash
010: Uncharted
...
=== changing filter ===Filter combination algorithm
While the manual filtering approach provides accurate results, it isn’t the most efficient method. Automating this process will significantly enhance both speed and efficiency. Let’s create a function that takes query parameters and a list of weighted filters as inputs and outputs a list of search hits.
Utility functions: the building blocks of filter manipulation
Before diving into the core function, it’s essential to create some utility functions to handle filter manipulation.
Negating filters
The negateFilter function, returns the opposite of a given filter. For example, if provided with genres = Animation, it would return NOT(genres = Animation).
function negateFilter(filter) {
return `NOT(${filter})`;
}
Aggregating filters
The aggregateFilters function combines two filter strings with an “AND” operation. For instance, if given genres = Animation and release_date > 1609510226, it would return (genres = Animation) AND (release_date > 1609510226).
function aggregateFilters(left, right) {
if (left === "") {
return right;
}
if (right === "") {
return left;
}
return `(${left}) AND (${right})`;
}
Generating combinations
The getCombinations function generates all possible combinations of a specified size from an input array. This is crucial for creating different sets of filter combinations based on their assigned weights.
function getCombinations(array, size) {
const result = [];
function generateCombination(prefix, remaining, size) {
if (size === 0) {
result.push(prefix);
return;
}
for (let i = 0; i < remaining.length; i++) {
const newPrefix = prefix.concat([remaining[i]]);
const newRemaining = remaining.slice(i + 1);
generateCombination(newPrefix, newRemaining, size - 1);
}
}
generateCombination([], array, size);
return result;
}The core function: boostFilter
Now that we have our utility functions, we can now move on to generating filter combinations in a more dynamic fashion, according to their assigned weights. This is achieved with the boostFilter function, it combines and sorts filters based on their respective weights.
function boostFilter(filterWeights) {
const totalWeight = filterWeights.reduce((sum, { weight }) => sum + weight, 0);
const weightScores = {};
const indexes = filterWeights.map((_, idx) => idx);
for (let i = 1; i <= filterWeights.length; i++) {
const combinations = getCombinations(indexes, i);
for (const filterIndexes of combinations) {
const combinationWeight = filterIndexes.reduce((sum, idx) => sum + filterWeights[idx].weight, 0);
weightScores[filterIndexes] = combinationWeight / totalWeight;
}
}
const filterScores = [];
for (const [filterIndexes, score] of Object.entries(weightScores)) {
let aggregatedFilter = "";
const indexesArray = filterIndexes.split(",").map(idx => parseInt(idx));
for (let i = 0; i < filterWeights.length; i++) {
if (indexesArray.includes(i)) {
aggregatedFilter = aggregateFilters(aggregatedFilter, filterWeights[i].filter);
} else {
aggregatedFilter = aggregateFilters(aggregatedFilter, negateFilter(filterWeights[i].filter));
}
}
filterScores.push([aggregatedFilter, score]);
}
filterScores.sort((a, b) => b[1] - a[1]);
return filterScores;
}
Breaking down the boostFilter function
Let’s dissect the function to better understand its components and operations.
1. Calculate total weight
The function begins by calculating the totalWeight, which is simply the sum of all the weights in the filterWeights array.
const totalWeight = filterWeights.reduce((sum, { weight }) => sum + weight, 0);2. Create weight and indexes structures
Two essential structures are initialized here:
weightScores: holds combinations of filters and their associated relative scoresindexes: an array that maps each filter to its position in the original filterWeights array
const weightScores = {};
const indexes = filterWeights.map((_, idx) => idx);3. Computation of weighted filter combinations
For each combination, we calculate its weight and store its relative score in the weightScores object.
for (let i = 1; i <= filterWeights.length; i++) {
const combinations = getCombinations(indexes, i);
for (const filterIndexes of combinations) {
const combinationWeight = filterIndexes.reduce((sum, idx) => sum + filterWeights[idx].weight, 0);
weightScores[filterIndexes] = combinationWeight / totalWeight;
}
}
4. Aggregate and negate filters
Here, we form the aggregated filter string. Each combination from weightScores is processed and populated into the filterScores list, along with its relative score.
const filterScores = [];
for (const [filterIndexes, score] of Object.entries(weightScores)) {
let aggregatedFilter = "";
const indexesArray = filterIndexes.split(",").map(idx => parseInt(idx));
for (let i = 0; i < filterWeights.length; i++) {
if (indexesArray.includes(i)) {
aggregatedFilter = aggregateFilters(aggregatedFilter, filterWeights[i].filter);
} else {
aggregatedFilter = aggregateFilters(aggregatedFilter, negateFilter(filterWeights[i].filter));
}
}
filterScores.push([aggregatedFilter, score]);
}
5. Sort and return filter scores
Finally, the filterScores list is sorted in descending order based on scores. This ensures the most “important” filters (as determined by weight) are at the beginning.
filterScores.sort((a, b) => b[1] - a[1]);
return filterScores;Using the filter boosting function
Now that we have the boostFilter function, we can demonstrate its efficacy on an example. This function returns an array of arrays, where each inner array contains:
- A combined filter based on the input criteria
- A score indicating the weighted importance of the filter
When we apply our function to an example:
boostFilter([["genres = Animation", 3], ["genres = Family", 1], ["release_date > 1609510226", 10]])We receive the following output:
[
[
'((genres = Animation) AND (genres = Family)) AND (release_date > 1609510226)',
1
],
[
'((genres = Animation) AND (NOT(genres = Family))) AND (release_date > 1609510226)',
0.9285714285714286
],
[
'((NOT(genres = Animation)) AND (genres = Family)) AND (release_date > 1609510226)',
0.7857142857142857
],
[
'((NOT(genres = Animation)) AND (NOT(genres = Family))) AND (release_date > 1609510226)',
0.7142857142857143
],
[
'((genres = Animation) AND (genres = Family)) AND (NOT(release_date > 1609510226))',
0.2857142857142857
],
[
'((genres = Animation) AND (NOT(genres = Family))) AND (NOT(release_date > 1609510226))',
0.21428571428571427
],
[
'((NOT(genres = Animation)) AND (genres = Family)) AND (NOT(release_date > 1609510226))',
0.07142857142857142
]
]
Generating search queries from boosted filters
Now that we have a prioritized list of filters from the boostFilter function, we can use it to generate search queries.Let’s create a searchBoostFilter function to automate the generation of search queries based on the boosted filters and execute the search queries using the provided Meilisearch client.
async function searchBoostFilter(client, filterScores, indexUid, q) {
const searchQueries = filterScores.map(([filter, _]) => {
const query = { ...q };
query.indexUid = indexUid;
query.filter = filter;
return query;
});
const results = await client.multiSearch({ queries: searchQueries });
return results;
}The function takes the following parameters
client: the Meilisearch client instance.filterScores: array of arrays of filters and their corresponding scores.indexUid: the index you want to search withinq: base query parameters
For each filter in filterScores, we:
- create a copy of the base query parameters
qusing the spread operator - update the
indexUidandfiltervalues for the current search query - add the modified
queryto oursearchQueriesarray
The function then returns the raw results from the multi search route.
Example: extracting top movies using filter scores
Let’s create a function to display the top movie titles that fit within our defined search limits and based on our prioritized filter criteria: the bestMoviesFromFilters function.
async function bestMoviesFromFilters(client, filterWeights, indexUid, q) {
const filterScores = boostFilter(filterWeights);
const results = await searchBoostFilter(client, filterScores, indexUid, q);
const limit = results.results[0].limit;
let hitIndex = 0;
let filterIndex = 0;
for (const resultsPerIndex of results.results) {
if (hitIndex >= limit) {
break;
}
const [filter, score] = filterScores[filterIndex];
console.log(`=== filter '${filter}' | score = ${score} ===`);
for (const result of resultsPerIndex.hits) {
if (hitIndex >= limit) {
break;
}
console.log(`${String(hitIndex).padStart(3, '0')}: ${result.title}`);
hitIndex++;
}
filterIndex++;
}
} The function uses the boostFilter function to get the list of filter combinations and their scores.
Then, the searchBoostFilter function obtains the results for the provided filters.
It also determines the maximum number of movie titles we wish to display based on the limit set in our base query.
Using a loop, the function iterates through the results:
- If the current count of displayed movie titles (
hitIndex) reaches the specifiedlimit, the function stops processing further. - For each set of results from the multi-search query, the function displays the applied filter condition and its score.
- It then goes through the search results (or hits) and displays the movie titles until the
limitis reached or all results for the current filter are displayed. - The process continues for the next set of results with a different filter combination until the overall
limitis reached or all results are displayed.
Let’s use our new function in an example:
bestMoviesFromFilters(client,
[
{ filter: "genres = Animation", weight: 3 },
{ filter: "genres = Family", weight: 1 },
{ filter: "release_date > 1609510226", weight: 10 }
],
"movies",
{ q: "Samurai", limit: 100 }
)
We get the following output:
=== filter '((genres = Animation) AND (genres = Family)) AND (release_date > 1609510226)' | score = 1.0 ===
000: Blazing Samurai
=== filter '((genres = Animation) AND (NOT(genres = Family))) AND (release_date > 1609510226)' | score = 0.9285714285714286 ===
=== filter '((NOT(genres = Animation)) AND (genres = Family)) AND (release_date > 1609510226)' | score = 0.7857142857142857 ===
=== filter '((NOT(genres = Animation)) AND (NOT(genres = Family))) AND (release_date > 1609510226)' | score = 0.7142857142857143 ===
=== filter '((genres = Animation) AND (genres = Family)) AND (NOT(release_date > 1609510226))' | score = 0.2857142857142857 ===
001: Scooby-Doo! and the Samurai Sword
002: Kubo and the Two Strings
=== filter '((genres = Animation) AND (NOT(genres = Family))) AND (NOT(release_date > 1609510226))' | score = 0.21428571428571427 ===
003: Samurai Jack: The Premiere Movie
004: Afro Samurai: Resurrection
005: Program
006: Lupin the Third: Goemon's Blood Spray
007: Hellboy Animated: Sword of Storms
008: Gintama: The Movie
009: Heaven's Lost Property the Movie: The Angeloid of Clockwork
010: Heaven's Lost Property Final – The Movie: Eternally My Master
=== filter '((NOT(genres = Animation)) AND (genres = Family)) AND (NOT(release_date > 1609510226))' | score = 0.07142857142857142 ===
011: Teenage Mutant Ninja Turtles IIIConclusion
In this guide, we walked through the process of implementing a scored filtering feature. We learned how to set up weighted filters and automatically generate filter combinations, which we then scored based on their weight. Following that, we explored how to create search queries using these boosted filters with the help of Meilisearch's multi-search API.
We plan to integrate scored filters in the Meilisearch engine. Give your feedback on the previous link to help us prioritize it.
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