Recommendations

How does Smart Recommender Work?

Smart Recommender's main focus is to show personalized and relevant content to users. Creating only one strategy for all users is not enough as users are getting more diversified day by day.

To make Smart Recommender work, you need to train the data about what users do and what they are interested in. This data is collected from desktop and mobile web which can also be called clickstream data via System Rules and is processed to be used on recommendations on the Web, Email, App, InStory, and Web Push. Additionally, you should also collect the product catalog of the stores in order to generate recommended product lists for each algorithm. You can do it in 3 different ways; Clickstream data, Catalog Integration through XML, and Catalog Integration through API.

• In Generic Algorithms, product recommendations are based on product performance rather than individual consumer behavior or current context.
• In Contextual Algorithms, product recommendations are based on the current contexts of individual consumers such as the product category of the current page, but not based on consumer behavior or activity.
• In Collaborative Algorithms, product recommendations are based on what similar users have engaged previously taking product visits, purchases, and added-to-carts into account. 
• In Multi-Strategy Algorithms, more than one algorithm performs to offer a related product recommendation to users. 

The available algorithms are:

• Chef
• Complementary Products
• Highest Discounted Products
• Manual Merchandising
• Mixed Strategy
• Most Popular Items
• Most Valuable Products
• New Arrivals
• Purchased Together
• Purchased with Last Purchased
• Recently Viewed Products
• Substitute Products
• Top Sellers
• Trending Products
• User Based
• Viewed Together

Recommendation API

Recommendation API provides algorithm results through separated algorithm endpoints in JSON responses of which content has been stitched with your product data. 

Algorithms have required and optional parameters that can be used on recommendation strategies based on needs. Before implementation, make sure to have the desired algorithms activated by Insider team.