With the multitude of eCommerce businesses nowadays, each company is trying to stand out. The more products you offer, the more extensive the customers’ choices, and a bigger chance they will find something relevant. But searching through thousands of products can be exhausting. For that reason, companies (the most known are e.g. Amazon, Netflix, Spotify) are using recommender systems, which allow customers to find the products relevant to them quicker and easier.
Our client, a fairly new home decor eCommerce business, wanted to create a product recommendations engine to increase the retention rate, achieve better sales, and make the buying process more convenient for its customers.
Most recommender systems take either of two basic approaches: collaborative filtering or content-based filtering. Other methods, such as demographic-based or hybrid approaches, also exist. We chose to mix collaborative and content-based ones.
Collaborative filtering is a recommendation based on a model of prior user behavior. It uses knowledge of other users to form a recommendation. This is based on the assumption that if user A likes chocolate (or in our example a golden lamp) and user B like chocolate (golden lamp) and popcorn (red curtains), then user A will also like popcorn (red curtains). Essentially, recommendations are based on the collaboration of multiple users, based on which you can filter those who show similar preferences.
Content-based filtering is based on the user’s behavior. This relies on the product’s characteristics. So if a user often buys a specific product (e.g., almond chocolate by company A), we will show him/her the same product by another company (almond chocolate by company B) and also similar products by different companies (hazelnut chocolate).
Therefore, product recommendation runs on predictive analytics, meaning correlating information and making predictions based on it.
Additionally to creating the recommender system, we added recommendation-related options to our client’s website: