Retail Engine, 2026
From version 21.1
edited by Anastasia Zanina
on 2026/09/08 02:35
Change comment: Deleted image "product_recommendations_overview.png"
To version 23.1
edited by Anastasia Zanina
on 2026/09/08 08:58
Change comment: Renamed back-links.

Summary

Details

Page properties
Parent
... ... @@ -1,1 +1,1 @@
1 -Main.Loymax_AI.WebHome
1 +Main.Retail_Engine_AI.WebHome
Content
... ... @@ -16,7 +16,7 @@
16 16  * Achieve business objectives;
17 17  * Automate marketing activities and test hypotheses.
18 18  
19 -Product recommendations are generated using the [[Retail Engine AI>>doc:Main.Loymax_AI.WebHome]] module whose algorithms enable fast and accurate processing and analysis of large data volumes. Marketers and analysts can focus on developing business strategies while delegating technical big-data processing tasks to artificial intelligence for specific business purposes.
19 +Product recommendations are generated using the [[Retail Engine AI>>doc:Main.Retail_Engine_AI.WebHome]] module whose algorithms enable fast and accurate processing and analysis of large data volumes. Marketers and analysts can focus on developing business strategies while delegating technical big-data processing tasks to artificial intelligence for specific business purposes.
20 20  
21 21  Business goals achieved through personalized communications using product recommendations:
22 22  
... ... @@ -293,7 +293,7 @@
293 293  The **Personalized Product Recommendations** algorithm is an ensemble of multiple models and algorithms. Its foundation is the [[Collaborative Filtering>>https://en.wikipedia.org/wiki/Collaborative_filtering]] technique.
294 294  Using this method within an ensemble of models—alongside other algorithms—produces more relevant product recommendations for each customer.
295 295  
296 -**Collaborative filtering** is a method for generating predictions (recommendations) in [[recommendation systems>>doc:Main.General_information.Loymax_Loyalty.Recommendation_systems.WebHome]] by leveraging known preferences (ratings) from a group of customers to predict unknown preferences for another customer.
296 +**Collaborative filtering** is a method for generating predictions (recommendations) in [[recommendation systems>>doc:Main.General_information.Retailengine_Loyalty.Recommendation_systems.WebHome]] by leveraging known preferences (ratings) from a group of customers to predict unknown preferences for another customer.
297 297  
298 298  The core assumption of this method is: customers who have purchased similar products/categories in the past are likely to make similar future purchases of other products they haven’t bought yet—but that their nearest “neighbors” (i.e., customers with highly similar purchase histories) have purchased.
299 299  
... ... @@ -307,7 +307,7 @@
307 307  **See also:**
308 308  
309 309  * [[Configuring product recommendations>>doc:Main.Smart_Communications.SMC_Use.Recommendations.WebHome]]
310 -* [[Recommendation System Integration>>doc:Main.General_information.Loymax_Loyalty.Recommendation_systems.WebHome]]
310 +* [[Recommendation System Integration>>doc:Main.General_information.Retailengine_Loyalty.Recommendation_systems.WebHome]]
311 311  * [[Omnichannel strategy>>doc:Main.General_information.Omnichannel.WebHome]]
312 312  * [[Attributes Related to Retail Engine AI>>doc:Main.Usage.MMP.Admin_panel.Customer_attributes.Attributes.WebHome||anchor="ML"]]
313 313  * [[Personal Offers Using Machine Learning Mechanics>>doc:Main.Installation_and_configuration.Extra_modules.CommunicationService_ML.WebHome]]
Created 2025/10/22 03:38
Last edited 2026/09/09 09:38

News
Updates
Tag cloud
Glossary
Site