Intelligent product recommendation systems, or recommender systems, refer to information filtering systems that have a goal to foresee user’s preferences in certain products. For example, Spotify creates a new playlist of songs specifically for every user by using their lists of favorite artists and music genres.
In ecommerce, recommender systems are based on user’s ratings, wishlists, and viewed items to offer a similar product from the same category. Customers can see items generated by recommender systems in the “Customers who bought this item also bought…” section.
Recommendation systems usually process data in 4 phases, which are collecting, storing, analyzing, and filtering.
1.Data collection. At this stage, the system gathers data regarding existing customers. There are two types of data, explicit and implicit. Explicit data is the data provided by users themselves (ratings, comments); implicit data consists of clicks, page views, cart events, etc.
2.Data storage. All users have different tastes and preferences. That’s why the system collects and stores data to gradually learn more about the customer’s buying behavior and what categories interest him the most to provide relevant recommendations.
3.Data analysis. Sometimes there’s a need to provide recommendations for the item the user is currently viewing. For such cases, the system needs to provide a quick analysis for the viewed product. There are several ways for the system to analyze this kind of data:
4.Data filtering. This is the final stage, where the system provides users with relevant recommendations after filtering the data. There are 2 types of filtering:
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