Powered by artificial intelligence (AI), search and recommendation algorithms shape our interaction (and satisfaction) with online platforms. Developed to predict user choices, preferences and behaviours, its purpose is to improve the overall user experience of websites, apps, smart assistants and other types of computer programmes.
From Google to Amazon and Netflix, today’s biggest online retailers and service providers are making use of this class of machine learning to improve business conversion and retention rates: pushing products, boosting repeat sales and keeping customers happy and engaged.
How do search and recommendation algorithms work?
Where search algorithms work to retrieve relevant data, information and services in reaction to a user query, recommendation algorithms suggest similar alternate results based upon the user’s search (or purchase) history.
Put simply, search algorithms assist users in finding exactly what they want, while recommendation algorithms help users find more of what they like.
A search algorithm locates specific data within a larger collection of data. According to Internet Live Stats, on average, Google processes over 100,000 search queries per second. That’s an immense demand on a system, but while 98% of all internet users frequent a search engine monthly, it’s imperative that they be built to produce accurate results quickly and efficiently.
Basic site-search has quickly become an essential feature of almost any website, while the search function is considered a fundamental procedure in computing overall (extending to coding, development and data science). Intended to keep all types of users happy and informed, search algorithms step in to get the right resources in front of the right people.
All search algorithms operate via a search key (or bar) and work by returning a success or failure status based on the entered information. They break a query down into separate words, and using text-matching, link those words to matching titles and descriptions in the data sets.
Different search algorithms vary in terms of performance and efficiency, depending on how they are used and the available data. Some of the more commonly used search algorithms include:
- linear search algorithm
- binary search algorithm
- depth-first search algorithm
- breadth-first search algorithm
More complex algorithms can identify typing and auto-correct mistakes, as well as offering synonym recognition. Advanced algorithms can produce more refined results, factoring in popular answers, product rankings and other key metrics.
Google’s search algorithm
Google attributes its success to meticulous testing and complex search experiments. A combination of its infamous crawling “spider” bots, data-driven indexing and rigorous ranking system enables the search engine to meet its exemplary standards of relevance and quality.
Analysing everything from sitemap to content, images and URLs used, Google is able to identify the best pages to signpost your query in a fraction of a second. The search engine even boasts a freshness algorithm that, in response to trending topics and keywords, shows users the most up-to-date online articles available in realtime.
Good search engines boast another important feature: related results. This can make the difference between a bounce and purchase as customers are encouraged to keep browsing the site. This is where recommendation algorithms become useful.
Recommendation algorithms rely on data science to filter and recommend personalised suggestions (whether that be related search results or product recommendations) based on a user’s previous actions. Recommendation algorithms can generally be separated into two types: content-based filtering and collaborative filtering.
These algorithms factor in information (such as keywords and attributes) of both the user and the chosen item or product profile to generate recommendations. By utilising the customer’s personal data (such as gender, occupation and more), content-based filtering algorithms are able to assess popular products by age-group or locale, for example. Similarly, by analysing the product characteristics, the system can recommend other items with similar attributes. The more people that use the platform, the more data can be mined and improve the specificity of the suggestions.
The Netflix recommendation engine
Netflix states that recommendation algorithms are at the core of its product. In fact, 80% of viewer activity is driven by personalised recommendations from its engine. Netflix began experimenting with data as early as 2006 to improve the accuracy of its preference algorithms. As a result, the Netflix recommendation engine tracks numerous data points, from browsing behaviours to binge-watching habits, and filters over 3,000 titles at a time using 1,300 recommendation clusters based on user preferences. The platform has taken data beyond rating prediction and into personalised ranking, page generation, search, image selection, messaging, marketing, and more.
These algorithms accumulate data from all users on a platform and work like a word-of-mouth recommendation. By comparing datasets, such as purchase or rating information, the algorithms help the platform identify kindred customer profiles and recommend other products or services favoured by these ‘similar users’.
InData Labs notes the greatest merits of collaborative filtering systems as:
- capability of accurately recommending complex items (such as films, books or clothing) without requiring an “understanding” of the item itself
- basing recommendations on more personalised ‘similar’ users, without needing a more comprehensive knowledge of all products or all users of the platform
- ability to be applied to any domain and provide more versatile cross-domain recommendations
Why are search and recommendation algorithms so essential?
The number of digital purchases continues to climb each year, cementing e-commerce as an indispensable function of the global retail framework. And, in the world of online shopping, customers want accuracy, ease of use and appropriate suggestions.
For online businesses and service providers, some of the key benefits to using a search or recommendation algorithm include:
- improve the relevance of search results and reduce the time it takes to find specific products and services
- boost key metrics, including web visits and purchase rate, plus improve overall user loyalty and customer satisfaction
- aid in the selection process for an undecided customer, encourage them to interact with more products and and enter other potential purchases into their field of vision
- obtain data to target the right people with personalised ads and other digital marketing strategies to encourage users to frequent the website or platform
The quality of search and recommendation systems can significantly impact key business conversions, such as lead generations, customer sentiment scores and closing sales.
Amazon’s AI algorithms
As the leader of the global e-commerce market, Amazon is an almost-unrivalled product discovery and purchase platform, thanks to its optimal machine learning model. Built upon comprehensive ranking systems, the company’s A9 search algorithm analyses sales data, observes historical traffic patterns and indexes all product description text before a customer search query even begins, ensuring the best products are placed in front of the most likely buyers.
The platform’s combination of intelligent recommendation tools forms the personalised shopper experience that has become so popular with consumers.
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