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Product recommendation algorithms

BlueConic offers a variety of product recommendation algorithms to help you deliver personalized, one-to-one product recommendations based on individual customers' or visitors' behaviors, interests, and preferences.

To activate personalization recommendations, you use toolbar plugins for product recommendations recommendations. 

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This article provides notes on how product recommendation algorithms work. To add recommendations to your pages or app, refer to the Deliver 1:1 Product Recommendations Use Case article.

Algorithms overview

Algorithm Description Notes

Top products

("RECENT_VIEW")

 Use the top products algorithm to boost the products that are most viewed during a time frame you define.
  • Independent of profile
  • Based on statistics of the last X hours (default is 5 hours)
  • Products are boosted based on the value of the statistics field “view,” which is incremented whenever the product page is viewed (independent of recommendations).

Viral products

("RECENT_ENTRYPAGE")

 Use the viral products algorithm to boost the products that are most used as a landing page during the defined time frame.
  • Independent of profile
  • Based on statistics of the last X hours
  • Items are boosted based on the value of the statistics field “entrypage.”
Recent high CTRs ("RECENT_CTR") Use the recent high CTR algorithm to boost the most clicked-through products from BlueConic recommendations during the defined time frame.
  • Independent of profile
  • Based on statistics of the last X hours
  • Items are boosted based on the value of the statistics field “click” (someone clicks a product in a recommendations dialogue) divided by the value of the field “recommendation_view” (an item is recommended).
Same category ("SAME_CATEGORY") Use the same category algorithm to boost products of the same category as the page the visitor is currently watching.
  • Independent of profile
  • Independent of statistics
  • Items are boosted based on the values of the “categories” metadata field of the “current item” versus the categories of the other items. If items have multiple categories, the score is higher if they have more categories in common.
Look-alike products ("LOOK_ALIKE") Use the look-alike product algorithm to boost products that have similar content to the one someone is currently viewing.
  • Independent of profile
  • Independent of statistics
  • Look-alike algorithms use a TF-IDF weighting factor that measures term frequency and significance to determine which items are most similar to the current item. This determination is based on the words in the product item's “name,” “description,” and “text” metadata fields.

Recency
("RECENCY")

 Use the recency algorithm to boost the most recently added products.
  • Independent of profile
  • Independent of statistics
  • This personalization algorithm uses the value of your products' “publicationDate” metadata field to boost items. Newer items score higher than older ones.
Collaborative filtering (“COLLABORATIVE_FILTERING") Use the collaborative filtering algorithms to make personalized product recommendations for individuals based on the browsing behavior of other people who view the same products.
  • Independent of statistics
  • Dependent on the items the individual has viewed, put in a shopping cart, or ordered
  • Weight values: view=1, shopping cart=5, order=25
  • Operates with a ramp-up time, taking time to account for the interests of other viewers.
  • This results in an affinity between profiles and items, which is used in a collaborative filtering model.
Same interest
("INTEREST")		 Use the same interest algorithm to boost products that are similar to those the customer or visitor already viewed, based on the values of the item's “categories” metadata fields.
  • Independent of statistics
  • Dependent on the items the customer or visitor has viewed
  • Operates with a ramp-up time
Seen products ("RECENTLY_VIEWED") Use the seen products algorithm to boost products the customer or visitor recently viewed.
  • Independent of statistics
  • Dependent on the items the customer or visitor has viewed
  • The items the individual most recently viewed are boosted the most. You can use this algorithm to create a "last viewed products" overview.

Shopping cart-based recommendations

For personalized product recommendations, there are similar algorithms for items the customer recently placed in the shopping cart and items they have recently ordered: "RECENTLY_SHOPPINGCART" and "RECENTLY_BOUGHT", as well as algorithms that boost products most often placed in the cart or most frequently bought, during a certain time frame ("RECENT_SHOPPINGCART" and "RECENT_ORDER").

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