We, as humans, make purchasing decisions every day. Most of the products we buy are either something we like or something that our friends like. A study by Harvard Business School assessed a unique set of data from Cyword (a South Korean social networking site) and found that friends indeed influence user’s purchases. This social effect was found positive for 40% of the users and on average, it translated into a 5% boost in revenue . This is so because we tend to buy products that have been tried, tested, loved and recommended by our friends. In today’s digital era, any e-commerce marketplace you visit has some sort of recommendation algorithm. If this recommendation engine, algorithm or system is set up properly, it can accelerate Click Through Rates (CTRs), conversions, revenue and other important Key Performance Indicators (KPIs). Additionally, recommendation algorithms can also have a positive effect on the user’s shopping experience, which ultimately contributes to your revenue and KPIs that are important for customer satisfaction and retention.
In this blog, we will try to explain what is a recommendation algorithm, how it works, what are its types and how different big tech companies, like Amazon, Facebook and Netflix, use these algorithms to their advantage. Let us begin by understanding the basics.
What is a recommendation algorithm?
Recommendation algorithms are, at the core, filtering tools that use data provided by users to recommend them similar or the most relevant items. These are machine learning systems that continuously learn from user behavior and help them discover products that might be the most suitable for them.
To break it down in easier terms, a recommendation algorithm works like a salesman in any offline showroom you visit. You go to a showroom and ask a salesman for a product you’re looking for. If you are a regular customer, he probably recognize you and might be acquainted with your taste and preferences. As a result, he gets you exactly what you want in addition to a few related (probably better) items you might consider purchasing. You keep going back to that showroom because you always get what you want or end up buying something better.
Recommender algorithms are like those salesmen who know what you are looking for and what you’re likely to buy, based on your search history, shopping history and preferences. Recommendation systems aren’t just used in online shopping or e-commerce platforms like Amazon for product recommendations. A few other examples of a recommendation algorithm in play includes suggestions for TV shows and movies on streaming services like Netflix, recommended videos on YouTube, Facebook newsfeed and music on Spotify.
For these algorithms to work, a huge amount of user data is collected by the platforms. But what exactly is the data collected by them? Let’s find out.
Data collected by platforms for the recommendation algorithm
For any recommendation engine, data is the oil that fuels it up and keeps it running. Be it Amazon, Netflix or Spotify, any tech platform or online shopping website collects a huge amount of data to understand the user’s preferences. Keeping these preferences in ‘mind’, the recommendation engines predict what the user might like and end up spending money on. The recommendation algorithm captures and uses three types of user data:
User Demographics Data
Demographic data includes your personal information such as location, age, gender, education, profession, etc.
User Behavior Data
All the necessary information about how you, as a user, interacts and engages with products showcased online. It takes into account the ratings you provide, the product listings you click on, the products you ‘Like’ and the products which you have actually purchased.
This data is directly related to the products you interact and engage with. Information like genre and cast (in case of TV shows, movies and books), cuisines (in case of food), type of clothing (in case of an e-commerce website for apparel) and so on, are taken into account.
How we provide data to the recommendation algorithm
We feed data and information in the recommendation algorithm by two major methods: Explicit Ratings and Implicit Ratings.
Explicit Ratings are the ratings actually provided by users on the products they have bought online. These ratings directly indicate the preferences of the users. Examples include star ratings, product or service reviews, likes, following and feedback. Relying solely on Explicit Ratings isn’t ideal because these are hard to get and users don’t always rate the products they purchase.
Implicit Rating is based on understanding users’ interaction with the products listed. It is about inferring user behavior and is easier to get as compared to Explicit Ratings. Examples of Implicit Ratings are product views, clicks, the devices you use and location.
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