Using content recommendation engines to increase sales and improve customer engagement

For any recommendation engine, data is always the starting point. The AI will need to collect user data through explicit and implicit means in order to feed the algorithm the information it needs to produce recommendations. Implicit data is collected from user behaviour such as demographics, purchase history, time spent reading a blog, adding to cart and adding to a list. Explicit data is collected from user input such as likes and dislikes, reviews and ratings or comments.

User profiles will be created based on demographic, psychographic and behavioural data. The next step is assigning attributes to each object (e.g., product or content) so the ML model can match user profiles with products or content that share similar attributes.

Where you store the collected data is vital in terms of not only security but also scalability. There are a number of options available to you based on the type of data being stored. Structured data can be stored in an SQL database while unstructured data can be stored in a NoSQL database. You could also opt to make use of cloud-based storage or a mixture of storage solutions. We can help you select the right data storage solution for maximum efficiency.

The third step involves setting up the system to analyze the data. There are three data analysis models to choose from:

• Data can be analyzed periodically in batches. For example, maybe you want to analyze which products are added to cart on a daily basis.

• If a 24-hour analysis is not a fast enough turnaround time, you can opt for near real-time analysis where data is processed every few seconds or minutes. This is helpful if you’d like to collect user data in a single browsing session.

• Finally, real-time analysis enables you to analyze data as it comes in and thus provide recommendations in real time.

The type of data analysis model you select will depend on how quickly you want the system to produce recommendations based on processed data.

Finally, we will select the appropriate filtering approach in order to train the machine learning model to make the correct recommendations. There are three filtering models that can be applied:

Collaborative filtering is a method of making predictions about the interests of a user by collecting preferences from many users based on their interactions with content or products. These interactions can be classified as adding products to their cart, disliking a series on Netflix or clicking on a Facebook ad. For example, if multiple users with similar user profiles added a specific product to their wishlist, the recommender system would start recommending this product to people with similar interests. There are two main types of collaborative filtering recommender systems: model-based and memory-based.

1.  Model-based methods use a specific ML algorithm to learn the relationships between items and users from the training data. This approach usually requires more computational resources than memory-based methods but can generate more accurate recommendations.
2. Memory-based methods store information about past user behaviour in an easily accessible format. This data is then used to calculate similarities between users or items and make recommendations based on these similarities. This approach is typically less resource intensive than model-based methods, but can result in less accurate recommendations.

Content-based filtering is a method of making recommendations based on the similarity between content attributes and user preferences. In the case of music recommendations, it could take specific attributes like genres, artists, labels and producers into consideration to suggest songs or albums the user may like based on tracks they have recently listened to. This filtering approach doesn’t require any previous user data, as it creates recommendations as it receives incoming information.

Hybrid filtering makes recommendations by combining collaborative filtering and content-based filtering. This approach is used by most businesses today as used alone, each method has its shortfall but when combined these challenges fall away. By combining both content based recommendations and collaborative filtering, the strengths of each method can be used to provide highly accurate recommendations, even for new products and content.