We've been hearing about recommendation systems and their growing effects on our daily lives. But what exactly are they? How do they work and how do they manage to understand us so well?
neural networks to generate better guesses.
Although companies use sophisticated recommendation systems for different intents, the basic goal of all recommendation systems is to increase user experience, improve sales, and improve customer loyalty through personalized offers.
Recommender systems we use every day
In recent years, recommender systems have evolved and created incredible user interfaces and front-end features. As a result, many companies and platforms have adopted recommendation systems to serve their customers. In addition to increasing the company's overall revenue, they also improve the customer experience.
People see and interact with recommendation systems every day, without even realizing it. These systems improve the overall user experience and allow users to discover products and services they may need but miss.
Examples of prominent recommendation algorithms are:
Google Products: Many Google products use recommendation systems to provide users with personalized results. The search company collects data such as browsing history, search settings, clicks, and user metadata to provide a personalized experience. For example, YouTube provides video recommendations to its users based on viewing history, subscriptions, and profile, among other criteria.
Spotify- The music streaming platform uses an AI-based recommendation engine to filter available content and recommend songs, albums, podcasts, and other content based on interest, listening history, and search history.
Amazon – Amazon uses a recommendation system to suggest offers and products to its customers. The company uses the Deep Scalable Sparse Tensor Network Engine (DSSTNE) and deep learning algorithms to suggest “frequently purchased together” items.
They also collaborate with Google and Facebook to promote their products, which means users can also get Amazon suggestions on these sites while browsing. They're called targeted ads, and they're a big part of Amazon's marketing strategy.
Netflix: Netflix leverages the data it collects from users' histories to suggest TV shows and movies to its viewers.
How recommender systems work.
Recommender systems use available data and machine learning/deep learning algorithms to create final suggestions. They follow a sequence of steps that includes the following:
1. Data collection
Companies gather different types of data, such as explicit and implicit data. Implicit data includes things like cart history, search logs, order history, and clicks. This information is easy to obtain and you can track it through standard user interaction.
Explicit data contains information such as comments, ratings, ratings, and likes. This information is difficult to obtain and requires extra steps on the part of the user. This is also difficult to quantify, which means extra programming must be done on the engineer's part to account for this data.
Along with this, companies also need user and product similarity data. They may collect user similarity data through user demographics, geographic locations, and general interest. This data is often sent to the company during registration.
Product similarity data can be generated through company and product listings on the company website.
2. Data storage
Once data is collected, it is stored in data repositories and warehouses. Once the repository has a sufficient amount of data, companies move to the next step.
3. Data analysis
After enough data is stored, it goes through preprocessing . Engineers apply engineering and data processing techniques to clean and organize data. This way, they are preparing the data for analysis. The selected data can be real-time or batch data.
4. Data filtering
The final step in the process helps you gain relevant insights from the pre-processed data. This process applies different algorithms and formulas to data based on business logic and specific usage requirements. Details for the same are listed in the next section.
Types of filtering for recommendation systems.
There are different ways to filter data for recommendations. One way is collaborative filtering, where you take into account things like user activities, behaviors, or preferences. This method is based exclusively on user-product interactions. The algorithms create a user-item interaction matrix and the recommendation results from the implementation of the matrix.
This method can be further divided into memory-based collaborative filtering (an approach that creates clusters of users and recommends similar items to all users in that cluster) and model-based collaborative filtering (an approach in which the algorithm checks items based on interaction and recommends similar items to those users). They are also called the user-to-user approach and the item-to-item approach.
Another method for filtering results is content-based filtering, which discovers similarities between items through classification or regression methods and uses them to recommend similar items. The underlying idea is that if consumers like a certain product or service, they will also like a similar product or service. This method is therefore called a user-centered approach. This method, however, has a high bias but low variance.
The recommendation system that caters to both methods is called a hybrid filtering model. This is because it considers both the user's interest and the product's characteristics to make the final recommendation.
Final thoughts
Recommendation engines are now widely adopted by most large companies that use product/service personalization to improve user experience and engagement. This also generates more revenue for companies and increases users' trust in brands. Companies that understand their users are the ones that make the final sale.
For example, if you are looking for a certain pair of shoes and you receive an email alert listing the same shoes at a discount, you are very likely to purchase them by clicking the first link that appears.
Recommender systems are a powerful tool for generating new business and increasing demand from existing customers. Many small and medium-sized organizations have started to understand the importance of user data analysis. User communication is key and companies that understand this will flourish in the future.
Source: BairesDev
