Retail Engine, 2026
From version 16.1
edited by Andrej Rylov
on 2026/03/25 10:03
Change comment: Renamed from xwiki:Main.General_information.Loymax_AI.Commercial_recommendations.WebHome
To version 24.1
edited by Mariia Safronova
on 2026/09/09 09:38
Change comment: There is no comment for this version

Summary

Details

Page properties
Parent
... ... @@ -1,1 +1,1 @@
1 -Main.General_information.Loymax_AI.WebHome
1 +Main.Retail_Engine_AI.WebHome
Author
... ... @@ -1,1 +1,1 @@
1 -XWiki.arylov
1 +XWiki.safronovams
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 [[Loymax AI>>doc:Main.General_information.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  
... ... @@ -34,15 +34,15 @@
34 34  
35 35  (% class="box warningmessage" %)
36 36  (((
37 -* To use these algorithms in **Product Recommendations** macros within [[Smart Communications>>doc:Main.Smart_Communications.SMC_Use.WebHome]], please contact Loymax specialists.
37 +* To use these algorithms in **Product Recommendations** macros within [[Smart Communications>>doc:Main.Smart_Communications.SMC_Use.WebHome]], please contact Retail Engine specialists.
38 38  * The Product Recommendations module is an optional, separately licensed feature available on a paid basis.
39 39  )))
40 40  
41 -=== **Interaction of Loymax infrastructure components for generating product recommendations** ===
41 +=== **Interaction of Retail Engine infrastructure components for generating product recommendations** ===
42 42  
43 -Data sources for receipts, customers, and products from the product catalog used to generate product recommendations include the **DWH BI** data warehouse and/or the **ClickHouse SmartCom** database management system. This data is transferred to the **Loymax AI** module, where it is processed and product recommendations are calculated using ML algorithms. The resulting recommendations are then delivered via API to **data marts**. The **API-proxy server** validates incoming requests and formats responses for Smart Communications. **Smart Communications** retrieves responses from the **API-proxy server**, enriches them with data (images, product names, links, prices, etc.), inserts them into predefined templates, and sends mass messagings containing the generated product recommendations to customers.
43 +Data sources for receipts, customers, and products from the product catalog used to generate product recommendations include the **DWH BI** data warehouse and/or the **ClickHouse SmartCom** database management system. This data is transferred to the **Retail Engine AI** module, where it is processed and product recommendations are calculated using ML algorithms. The resulting recommendations are then delivered via API to **data marts**. The **API-proxy server** validates incoming requests and formats responses for Smart Communications. **Smart Communications** retrieves responses from the **API-proxy server**, enriches them with data (images, product names, links, prices, etc.), inserts them into predefined templates, and sends mass messagings containing the generated product recommendations to customers.
44 44  
45 -|(% style="border-color:#ffffff; text-align:center" %){{lightbox image="product_recommendations_overview.png" width="1200"/}}
45 +|(% style="border-color:#ffffff; text-align:center" %){{lightbox image="product_recommendations_overview_1.png" width="1200"/}}
46 46  
47 47  Currently, 5 algorithms are available for generating product recommendations:
48 48  
... ... @@ -57,7 +57,7 @@
57 57  **Notes:**
58 58  
59 59  1. Since the product recommendation macro operates on a list of recommendations, specific items are inserted into the message body using the control structures shown in the table above. See an example of the **Product Recommendations** module control structure [[here>>doc:Main.Smart_Communications.SMC_Use.Recommendations.WebHome||anchor="HDisplayingrecommendationsinmessages"]].
60 -1. API methods for integration into client-facing services ([[Mobile Application>>doc:Main.General_information.Additional_services.Mobile_app.WebHome]], [[Personal Account>>doc:Main.General_information.Additional_services.Personal_account.WebHome]]) are provided upon separate request to Loymax staff.
60 +1. API methods for integration into client-facing services ([[Mobile Application>>doc:Main.General_information.Additional_services.Mobile_app.WebHome]], [[Personal Account>>doc:Main.General_information.Additional_services.Personal_account.WebHome]]) are provided upon separate request to Retail Engine staff.
61 61  )))
62 62  
63 63  Recommendations produced by each algorithm differ because they serve distinct business goals and are based on different models, algorithms, hyperparameters, etc. When selecting a specific algorithm and usage scenario, it’s essential to consider multiple factors:
... ... @@ -82,7 +82,7 @@
82 82  The **Popular Products** algorithm calculates a ranking across the [[entire*>>doc:||anchor="Star"]] product list. It recommends the top-N most popular products from each category based on each product’s position in the overall ranking.
83 83  This approach recommends a broader range of products (i.e., several items from different categories), which is preferable for achieving business goals.
84 84  Product rankings are calculated based on total units sold across all customer purchases—i.e., the most purchased items.
85 -**All purchases** include both online and offline transactions processed through Loymax.
85 +**All purchases** include both online and offline transactions processed through Retail Engine.
86 86  
87 87  ==== 2. Use cases for the Popular Products algorithm in personalized campaigns ====
88 88  
... ... @@ -130,7 +130,7 @@
130 130  It recommends the top-N products from the category.
131 131  
132 132  Product rankings are calculated based on total units sold across all customer purchases—i.e., the most purchased items.
133 -**All purchases** include both online and offline transactions processed through Loymax.
133 +**All purchases** include both online and offline transactions processed through Retail Engine.
134 134  
135 135  ==== 2. Use cases for the Popular Products in Category algorithm in personalized campaigns ====
136 136  
... ... @@ -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  
... ... @@ -307,10 +307,10 @@
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 -* [[Attributes Related to Loymax AI>>doc:Main.Usage.MMP.Admin_panel.Customer_attributes.Attributes.WebHome||anchor="ML"]]
313 -* [[Personal Offers Using Machine Learning Mechanics>>doc:Main.Installation_and_configuration.Extra_modules.CommunicationService_ML.WebHome]]
312 +* [[Attributes Related to Retail Engine AI>>doc:Main.Usage.MMP.Admin_panel.Customer_attributes.Attributes.WebHome||anchor="ML"]]
313 +* [[Personal Offers Using Machine Learning Mechanics>>doc:Main.Installation_and_configuration.Additional_modules_configuring.CommunicationService_ML.WebHome]]
314 314  )))
315 315  
316 316  (% class="box" %)
product_recommendations_overview.png
Author
... ... @@ -1,1 +1,0 @@
1 -XWiki.arylov
Size
... ... @@ -1,1 +1,0 @@
1 -37.2 KB
Content
product_recommendations_overview_1.png
Author
... ... @@ -1,0 +1,1 @@
1 +XWiki.zanina
Size
... ... @@ -1,0 +1,1 @@
1 +40.4 KB
Content
Created 2025/10/22 03:38
Last edited 2026/09/09 09:38

News
Updates
Tag cloud
Glossary
Site