Search by Algolia
Introducing new developer-friendly pricing
algolia

Introducing new developer-friendly pricing

Hey there, developers! At Algolia, we believe everyone should have the opportunity to bring a best-in-class search experience ...

Nick Vlku

VP of Product Growth

What is online visual merchandising?
e-commerce

What is online visual merchandising?

Eye-catching mannequins. Bright, colorful signage. Soothing interior design. Exquisite product displays. In short, amazing store merchandising. For shoppers in ...

Catherine Dee

Search and Discovery writer

Introducing the new Algolia no-code data connector platform
engineering

Introducing the new Algolia no-code data connector platform

Ingesting data should be easy, but all too often, it can be anything but. Data can come in many different ...

Keshia Rose

Staff Product Manager, Data Connectivity

Customer-centric site search trends
e-commerce

Customer-centric site search trends

Everyday there are new messages in the market about what technology to buy, how to position your company against the ...

Piyush Patel

Chief Strategic Business Development Officer

What is online retail merchandising? An introduction
e-commerce

What is online retail merchandising? An introduction

Done any shopping on an ecommerce website lately? If so, you know a smooth online shopper experience is not optional ...

Vincent Caruana

Sr. SEO Web Digital Marketing Manager

5 considerations for Black Friday 2023 readiness
e-commerce

5 considerations for Black Friday 2023 readiness

It’s hard to imagine having to think about Black Friday less than 4 months out from the previous one ...

Piyush Patel

Chief Strategic Business Development Officer

How to increase your sales and ROI with optimized ecommerce merchandising
e-commerce

How to increase your sales and ROI with optimized ecommerce merchandising

What happens if an online shopper arrives on your ecommerce site and: Your navigation provides no obvious or helpful direction ...

Catherine Dee

Search and Discovery writer

Mobile search UX best practices, part 3: Optimizing display of search results
ux

Mobile search UX best practices, part 3: Optimizing display of search results

In part 1 of this blog-post series, we looked at app interface design obstacles in the mobile search experience ...

Vincent Caruana

Sr. SEO Web Digital Marketing Manager

Mobile search UX best practices, part 2: Streamlining search functionality
ux

Mobile search UX best practices, part 2: Streamlining search functionality

In part 1 of this series on mobile UX design, we talked about how designing a successful search user experience ...

Vincent Caruana

Sr. SEO Web Digital Marketing Manager

Mobile search UX best practices, part 1: Understanding the challenges
ux

Mobile search UX best practices, part 1: Understanding the challenges

Welcome to our three-part series on creating winning search UX design for your mobile app! This post identifies developer ...

Vincent Caruana

Sr. SEO Web Digital Marketing Manager

Teaching English with Zapier and Algolia
engineering

Teaching English with Zapier and Algolia

National No Code Day falls on March 11th in the United States to encourage more people to build things online ...

Alita Leite da Silva

How AI search enables ecommerce companies to boost revenue and cut costs
ai

How AI search enables ecommerce companies to boost revenue and cut costs

Consulting powerhouse McKinsey is bullish on AI. Their forecasting estimates that AI could add around 16 percent to global GDP ...

Michelle Adams

Chief Revenue Officer at Algolia

What is digital product merchandising?
e-commerce

What is digital product merchandising?

How do you sell a product when your customers can’t assess it in person: pick it up, feel what ...

Catherine Dee

Search and Discovery writer

Scaling marketplace search with AI
ai

Scaling marketplace search with AI

It is clear that for online businesses and especially for Marketplaces, content discovery can be especially challenging due to the ...

Bharat Guruprakash

Chief Product Officer

The changing face of digital merchandising
e-commerce

The changing face of digital merchandising

This 2-part feature dives into the transformational journey made by digital merchandising to drive positive ecommerce experiences. Part 1 ...

Reshma Iyer

Director of Product Marketing, Ecommerce

What’s a convolutional neural network and how is it used for image recognition in search?
ai

What’s a convolutional neural network and how is it used for image recognition in search?

A social media user is shown snapshots of people he may know based on face-recognition technology and asked if ...

Catherine Dee

Search and Discovery writer

What’s organizational knowledge and how can you make it accessible to the right people?
product

What’s organizational knowledge and how can you make it accessible to the right people?

How’s your company’s organizational knowledge holding up? In other words, if an employee were to leave, would they ...

Catherine Dee

Search and Discovery writer

Adding trending recommendations to your existing e-commerce store
engineering

Adding trending recommendations to your existing e-commerce store

Recommendations can make or break an online shopping experience. In a world full of endless choices and infinite scrolling, recommendations ...

Ashley Huynh

Looking for something?

Enhance & Promote Results: Introducing Query Rules
facebookfacebooklinkedinlinkedintwittertwittermailmail

Today, we are releasing Query Rules, a new feature which enables you to modify, override, & enhance the behavior of the engine’s configured ranking for a subset of the queries. We wanted to share with you how we approached this key addition to our API, why we decided to build it, and explain the design steps leading to the release today.

Before we dive in, let’s look at a few examples of what you can do with Query Rules:

  • Transform descriptive text into a filter: for example, by automatically transforming a query word into its equivalent filter (“cheap” would become a filter price< 400; “red” a filter on the color…)
  • Merchandising: manually select a result for a specific query (e.g., decide that the latest iPhone 8 should be in the first position for the query iPhone)
  • Entities detection: detect entities (colors, brands…) in the queries and transform them into filters or boosts

Building upon our ranking formula

With Algolia, the same relevance algorithm is applied across the entire index. We expose features like Custom Ranking so that customers can customize the ranking strategy for their needs, and achieve a great relevance for the vast majority of queries. However, in the past few years, our customers started to bring to us examples of outlier queries.

We began compiling a list of these outlier situations. Here are a few examples:

  • When Apple releases a new iPhone, it should be in the first position of the list for the query “iPhone” (even though at this stage it has no views, no sales… no “logical” reason to be ranked high)
  • You need to get rid of a big stock of a specific model of a vacuum cleaner, so you’d like it to be promoted in the results (at least until enough units are sold)
  • The query “phone” naturally (according to the configured ranking strategy) retrieves feature phones instead of smartphones, but you’d like to highlight smartphones first
  • The query “red dress” matches with non-red results because the name of the brand begins by “red”
  • The query “cheap phone” doesn’t return any results (the records don’t contain the word cheap), and would behave better if “cheap” was transformed into a numeric filter on the price
  • Queries originating from users on mobile devices should highlight results that are mobile-friendly

There are hundreds of examples like this where having an exception to the general rule would make sense, either to improve the relevance, or to override the ranking for business reasons.

Thinking about our options

There are two main ways to address the types of exceptions we were seeing. The natural way to handle this would be to analyze the use cases one by one and add a configuration to the engine to handle each of them individually. We could, for example, develop a form of synonyms that would transform a word into a filter. Eventually, these settings would form a merchandising tool, allowing users to tweak and override the ranking logic.

We certainly had the experience on the team to execute on this approach. Several team members, including our founders, have used, or even built merchandising platforms prior to founding/working for Algolia. However, it is exactly because of this experience that we had doubts that this was the right approach:

  • Creating a new setting inside the engine for each exception quickly leads to a bloated solution: there are just too many varieties of merchandising strategies
  • Building a quality merchandising tool that would work for all of our users – each of whom have specific needs & exceptions – is virtually impossible
  • Multiplying the exceptions inevitably affects performance

More importantly, we wanted to do more than merchandising and address the needs of other industries. Media sites don’t use merchandising, but they still want to promote, for example, partner content. SaaS systems may want to improve the ranking by adding rules automatically based on the output of a machine learning tool.

To do this, we would need to be able to impact search results on a subset of queries in two distinct places:

  • Before the search query is processed, in order to override the query parameters
  • After the results are found, in order to modify the results list

The solution

What we came up with is a rules system for queries — or Query Rules — that sits inside the engine via two modules:

  • A query preprocessing module that will modify the search parameters before the search is processed by the engine
  • A results postprocessing module that will modify the results before they are sent back to our users

Each rule has the following pattern: IF the query contains X, THEN modify Y. A condition and a consequence:

  • If the query contains “iPhone”, add this result in the first position
  • If the query is “cheap phone”, replace the word “cheap” by a numeric filter price<200
  • If the query contains a color, filter by that color

This approach makes it simple to modify the behavior of a subset of the queries, without impacting the rest of the queries. We created two types of conditions, and seven types of consequences, which together allow us to handle a variety of exceptions, including:

  • Promote the latest iPhone on the query “iphone”
  • Filter on color white for the query “white dress”
  • Filter on price<400 for the query “cheap phone”
  • Display a promotional banner for the query Samsung
  • Remove the word “hotel” in a hotels search

Conclusion

With Query Rules, we’re bringing to Algolia the ability to handle queries on an individual basis by making exceptions on the regular ranking strategy.

We think the approach we took has a few interesting benefits:

  • It is low-level and flexible enough to adapt to a large variety of use-cases without requiring a lot of settings and configuration
  • It is exposed via an API, allowing both our customers and potential partners specialized on merchandising to build upon it
  • It doesn’t compromise performance for the sake of feature sets: you can add up to a million rules to an index, with little to no impact on search performance

In fact, we like to think of Query Rules as more than a feature: we like to think of it as an extension of our engine. We have designed it to be open-ended enough to allow our users to solve their own unique problems and push the boundaries of what is possible with search.

It’s been exciting to start sharing the feature with a few beta testers — we’ve been amazed at how easily they grasped its potential, and the combinations it allows.

The two conditions and seven consequences we are initially releasing are only the beginning — we look forward to getting your feedback and learning which ones you’d like us to add next!


Meanwhile, we put together an online workshop, where two of our team members will show you in practice how to create smarter search with Query Rules.
=> You can register here.

About the author
Nicolas Baissas

Smarter search strategies to engage your users

How to apply business logic for more relevant search results.

Image of Jason HarrisImage of Olivier Lance
Jason Harris - Developer AdvocateOlivier Lance - Solutions Engineer
Image of Jason Harris
Jason HarrisDeveloper Advocate
Image of Olivier Lance
Olivier LanceSolutions Engineer
Smarter search strategies to engage your users Watch the webinar

Recommended Articles

Powered byAlgolia Algolia Recommend

Algolia's top 10 tips to achieve highly relevant search results
product

Julien Lemoine

Co-founder & former CTO at Algolia

What is a search query and how is it processed by a search engine?
product

Catherine Dee

Search and Discovery writer

Inside the Algolia Engine Part 3 — Query Processing
engineering

Julien Lemoine

Co-founder & former CTO at Algolia