What is Intelligent Product Recommendation Systems?

Intelligent Product Recommendation Systems

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.

Phases of system processing

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:

Real-time, when the system processes data as soon as it is created.
Near-real-time, when the system gathers data during the same browsing session and refreshes the analytics for a few minutes or seconds
Batch analysis, when the system analyzes a considerable amount of data to later create a recommendation. The analysis provided is more thorough and the recommendation is mostly done via notifications or emails in this case.

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:

Cluster. In this case, the system groups similar items regardless of what other users have watched or liked in order to recommend a fitting item.
Content-based. This type of filtering follows customer’s actions like spent time in various categories, items clicked on, visited pages, and so on. Based on user profiles and product catalogs, the system generates recommendations.

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