Loymax, 2026
From version 7.1
edited by Andrej Rylov
on 2025/10/22 11:12
Change comment: Upload new image "Recom_02.png", version 1.2
To version 9.1
edited by Andrej Rylov
on 2025/10/22 11:31
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... ... @@ -5,7 +5,7 @@
5 5  {{toc depth="3" start="2"/}}
6 6  )))
7 7  
8 -=== **General Information** ===
8 +=== **General information** ===
9 9  
10 10  **Product recommendations** are a tool for creating effective customer communications via email campaigns, as well as widgets on the website and in the Mobile Application.
11 11  
... ... @@ -38,7 +38,7 @@
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 Loymax infrastructure components for generating product recommendations** ===
42 42  
43 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.
44 44  
... ... @@ -104,9 +104,9 @@
104 104  1. Mobile Application,
105 105  1. Personal Account.
106 106  
107 -=== **{{id name="01"/}}“Popular Products” Algorithm** ===
107 +=== **{{id name="01"/}}**Popular Products** algorithm** ===
108 108  
109 -==== 1. Description of the **Popular Products** Algorithm ====
109 +==== 1. Description of the Popular Products algorithm ====
110 110  
111 111  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.
112 112  This approach recommends a broader range of products (i.e., several items from different categories), which is preferable for achieving business goals.
... ... @@ -113,10 +113,8 @@
113 113  Product rankings are calculated based on total units sold across all customer purchases—i.e., the most purchased items.
114 114  **All purchases** include both online and offline transactions processed through Loymax.
115 115  
116 -==== 2. Use Cases for the **Popular Products** Algorithm in Personalized Campaigns ====
116 +==== 2. Use cases for the Popular Products algorithm in personalized campaigns ====
117 117  
118 -**Email Campaigns:**
119 -
120 120  (% class="table-bordered" style="width:1218px" %)
121 121  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:370px" %)**Trigger**|(% style="text-align:center; width:446px" %)**Use Case**
122 122  |(% style="width:399px" %)[[**Abandoned View**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41144043E44843543D43D44B43943F44043E44143C43E442440"]]|(% style="width:370px" %)Product viewed but not purchased|(% style="width:446px" %)(((
... ... @@ -135,7 +135,7 @@
135 135  After a defined time interval (t), send a reminder email about the abandoned cart along with the most popular products.
136 136  )))
137 137  
138 -(% style="color:inherit; font-family:Montserrat,sans-serif; font-size:20px; font-weight:600" %)3. Examples of Using the (% style="color:inherit; font-family:Montserrat,sans-serif; font-size:20px" %)**Popular Products**(% style="color:inherit; font-family:Montserrat,sans-serif; font-size:20px; font-weight:600" %) Algorithm for Website Personalization
136 +==== (% style="color:inherit; font-family:Montserrat,sans-serif; font-size:20px; font-weight:600" %)3. Examples of using the (% style="color:inherit; font-family:Montserrat,sans-serif; font-size:20px" %)Popular Products(% style="color:inherit; font-family:Montserrat,sans-serif; font-size:20px; font-weight:600" %) algorithm for website personalization(%%) ====
139 139  
140 140  (% class="table-bordered" style="width:1218px" %)
141 141  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:370px" %)**Trigger**|(% style="text-align:center; width:446px" %)**Use Case**
... ... @@ -146,15 +146,15 @@
146 146  |(% style="width:399px" %)[[**Current Category Page View**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41A43B43843543D44243F43E44143C43E44244043543B43F44043E43444343A44243E43244344E43A43044243543343E44043844EA043D430441430439442435"]]|(% style="width:370px" %)Category view|(% style="width:446px" %)**Popular Products** section.
147 147  Customer is browsing a category. The most popular products are displayed.
148 148  
149 -==== 4. General Concept of the **Popular Products** Algorithm ====
147 +==== 4. General concept of the Popular Products algorithm ====
150 150  
151 151  **Example**: Generating recommendations by selecting the top 1 most popular product from each category.
152 152  
153 153  |(% style="border-color:#ffffff; text-align:center" %)[[image:attach:Recommendations_01.png]]
154 154  
155 -=== **{{id name="02"/}}“Popular Products in Category” Algorithm** ===
153 +=== **{{id name="02"/}}Popular Products in Category algorithm** ===
156 156  
157 -==== 1. Description of the **Popular Products in Category** Algorithm ====
155 +==== 1. Description of the Popular Products in Category algorithm ====
158 158  
159 159  The **Popular Products in Category** algorithm calculates a popularity ranking among products within each specific category.
160 160  
... ... @@ -163,20 +163,18 @@
163 163  Product rankings are calculated based on total units sold across all customer purchases—i.e., the most purchased items.
164 164  **All purchases** include both online and offline transactions processed through Loymax.
165 165  
166 -==== 2. Use Cases for the **Popular Products in Category** Algorithm in Personalized Campaigns ====
164 +==== 2. Use cases for the Popular Products in Category algorithm in personalized campaigns ====
167 167  
168 168  Recommended products are selected based on the customer’s previously viewed/ordered/purchased items and the category (i.e., category ID) to which those items belong.
169 169  
170 170  The category/classifier level assigned to a product can be either the lowest or a higher level in the hierarchy.
171 171  
172 -**Email Campaigns:**
173 -
174 174  (% class="table-bordered" style="width:1218px" %)
175 175  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:370px" %)**Trigger**|(% style="text-align:center; width:446px" %)**Use Case**
176 176  |(% style="width:399px" %)[[**Abandoned View**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41144043E44843543D43D44B43943F44043E44143C43E442440"]]|(% style="width:370px" %)Product viewed but not purchased|(% style="width:446px" %)Send an email after X days recommending the most popular products in the same category.
177 177  |(% style="width:399px" %)[[**Abandoned Cart**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41144043E44843543D43D43044F43A43E44043743843D430"]]|(% style="width:370px" %)Product added to cart but not purchased|(% style="width:446px" %)Send an email after X days recommending the most popular products in the same category.
178 178  
179 -==== 3. Use Cases for the **Popular Products in Category** Algorithm in Website Personalization ====
175 +==== 3. Use cases for the Popular Products in Category algorithm in website personalization ====
180 180  
181 181  Recommended products are selected based on the customer’s on-site behavior (event tracking) and the category (i.e., category ID) of the viewed product. The classifier level (i.e., category) can be configured—it may be the lowest level or a higher-level category.
182 182  
... ... @@ -185,7 +185,7 @@
185 185  |(% style="width:399px" %)[[**Current Product Page View**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41A43B43843543D44243F43E44143C43E44244043543B43F44043E43444343A442A043D430441430439442435"]]|(% style="width:369px" %)Product view|(% style="width:447px" %)**Compared With This Product** section.
186 186  Recommend popular products from the same category based on the currently viewed item.
187 187  
188 -==== 4. General Concept of the Algorithm ====
184 +==== 4. General concept of the algorithm ====
189 189  
190 190  Product rankings are calculated at each classifier level (**Group ID**, **Subgroup ID**, **Category ID**) across all products belonging to that hierarchical level—i.e., all items in the group/subgroup/category—regardless of deeper nesting.
191 191  
... ... @@ -195,24 +195,22 @@
195 195  
196 196  |(% style="border-color:#ffffff; text-align:center" %)[[image:attach:Recom_02.png]]
197 197  
198 -=== **{{id name="03"/}}“Similar Products” Algorithm** ===
194 +=== **{{id name="03"/}}Similar Products algorithm** ===
199 199  
200 -==== 1. Description of the **Similar Products** Algorithm ====
196 +==== 1. Description of the Similar Products algorithm ====
201 201  
202 202  The **Similar Products** algorithm recommends alternative [[products*>>doc:||anchor="Star"]] that are the most similar to the viewed/ordered/purchased item.
203 203  
204 204  It recommends N products that have similar attributes to the viewed/ordered/purchased product.
205 205  
206 -==== 2. Use Cases for the **Similar Products** Algorithm in Personalized Campaigns ====
202 +==== 2. Use cases for the Similar Products algorithm in personalized campaigns ====
207 207  
208 -**Email Campaigns:**
209 -
210 210  (% class="table-bordered" style="width:1218px" %)
211 211  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:370px" %)**Trigger**|(% style="text-align:center; width:446px" %)**Use Case**
212 212  |(% style="width:399px" %)[[**Abandoned View**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41144043E44843543D43D44B43943F44043E44143C43E442440"]]|(% style="width:370px" %)Product viewed but not ordered|(% style="width:446px" %)Send an email after X days with alternative products similar to those viewed by the customer.
213 213  |(% style="width:399px" %)[[**Abandoned Cart**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41144043E44843543D43D43044F43A43E44043743843D430"]]|(% style="width:370px" %)Product added to cart but not purchased|(% style="width:446px" %)Send an email after X days with alternative products similar to those viewed by the customer.
214 214  
215 -==== 3. Use Cases for the **Similar Products** Algorithm in Website Personalization ====
209 +==== 3. Use cases for the Similar Products algorithm in website personalization ====
216 216  
217 217  (% class="table-bordered" style="width:1218px" %)
218 218  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:369px" %)**Trigger**|(% style="text-align:center; width:447px" %)**Use Case**
... ... @@ -219,7 +219,7 @@
219 219  |(% style="width:399px" %)[[**Current Product Page View**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41A43B43843543D44243F43E44143C43E44244043543B43F44043E43444343A442A043D430441430439442435"]]|(% style="width:369px" %)Product view|(% style="width:447px" %)**You Might Like** section.
220 220  Recommend alternative products similar to the one currently viewed by the customer.
221 221  
222 -==== 4. General Concept of the Algorithm ====
216 +==== 4. General concept of the algorithm ====
223 223  
224 224  |(% style="border-color:white; width:368px" %)[[image:attach:ARL_rules_example.gif]]|(% style="border-color:white; width:1070px" %)(((
225 225  For each product, a neural network generates an N-dimensional vector representation (embedding) based on product descriptions, attributes, purchase/view statistics/content.
... ... @@ -231,16 +231,14 @@
231 231  Similar products are selected based on minimal cosine distance between vectors—i.e., those sharing the most similar combination of product attributes.
232 232  )))
233 233  
234 -=== **{{id name="04"/}}“Bought Together” Algorithm** ===
228 +=== **{{id name="04"/}}Bought Together algorithm** ===
235 235  
236 -==== 1. Description of the **Bought Together** Algorithm ====
230 +==== 1. Description of the Bought Together algorithm ====
237 237  
238 238  The **Bought Together** algorithm recommends products that most frequently appear in the same transaction as the viewed/ordered/purchased items.
239 239  
240 -==== 2. Use Cases for the **Bought Together** Algorithm in Personalized Campaigns ====
234 +==== 2. Use cases for the Bought Together algorithm in personalized campaigns ====
241 241  
242 -**Email Campaigns:**
243 -
244 244  (% class="table-bordered" style="width:1218px" %)
245 245  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:370px" %)**Trigger**|(% style="text-align:center; width:446px" %)**Use Case**
246 246  |(% style="width:399px" %)[[**Abandoned View**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41144043E44843543D43D44B43943F44043E44143C43E442440"]]|(% style="width:370px" %)Product viewed but not ordered|(% style="width:446px" %)Send an email after X days with products most frequently bought together with the item viewed by the customer.
... ... @@ -247,7 +247,7 @@
247 247  |(% style="width:399px" %)[[**Abandoned Cart**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41144043E44843543D43D43044F43A43E44043743843D430"]]|(% style="width:370px" %)Product added to cart but not purchased|(% style="width:446px" %)Send an email after X days with products most frequently bought together with the items in the customer’s cart.
248 248  |(% style="width:399px" %)[[**Thank You for Your Purchase!**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H42143443543B43043D43743043A430437"]]|(% style="width:370px" %)Customer made a purchase|(% style="width:446px" %)Send an email after X days with products most frequently bought together with the items in the customer’s order.
249 249  
250 -==== 3. Use Cases for the **Bought Together** Algorithm in Website Personalization ====
242 +==== 3. Use cases for the Bought Together algorithm in website personalization ====
251 251  
252 252  (% class="table-bordered" style="width:1218px" %)
253 253  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:369px" %)**Trigger**|(% style="text-align:center; width:447px" %)**Use Case**
... ... @@ -256,7 +256,7 @@
256 256  |(% style="width:399px" %)[[**Product Added to Cart**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41443E43143043243B43543D44243E43243044043243A43E43D44243543943D435440"]]|(% style="width:369px" %)Product added to cart|(% style="width:447px" %)**Frequently Bought Together** section.
257 257  Recommend complementary products.
258 258  
259 -==== 4. General Concept of the **Bought Together** Algorithm ====
251 +==== 4. General concept of the Bought Together algorithm ====
260 260  
261 261  This algorithm incorporates a series of models and techniques based on the **[[Association Rule Learning>>https://en.wikipedia.org/wiki/Association_rule_learning]]** methodology.
262 262  
... ... @@ -291,9 +291,9 @@
291 291  * Pears to a customer who purchased Brie cheese (Rule 3).
292 292  )))
293 293  
294 -=== **{{id name="05"/}}“Personalized Product Recommendations” Algorithm** ===
286 +=== **{{id name="05"/}}Personalized Product Recommendations algorithm** ===
295 295  
296 -==== 1. Description of the **Personalized Product Recommendations** Algorithm ====
288 +==== 1. Description of the Personalized Product Recommendations algorithm ====
297 297  
298 298  (% class="wikigeneratedid" id="H" %)
299 299  The **Personalized Product Recommendations** algorithm generates product suggestions based on an individual customer’s behavior (Customer ID) and the behavior of similar customers.
... ... @@ -302,10 +302,8 @@
302 302  (% class="wikigeneratedid" %)
303 303  To predict optimal customer preferences, the machine learning model uses data on customer behavior and purchase history, online activity, customer profiles and attributes, product attributes and statistics, and other parameters.
304 304  
305 -==== 2. Use Cases for the **Personalized Product Recommendations** Algorithm in Personalized Campaigns ====
297 +==== 2. Use cases for the Personalized Product Recommendations algorithm in personalized campaigns ====
306 306  
307 -**Email Campaigns:**
308 -
309 309  (% class="table-bordered" style="width:1218px" %)
310 310  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:370px" %)**Trigger**|(% style="text-align:center; width:446px" %)**Use Case**
311 311  |(% style="width:399px" %)(((
... ... @@ -318,7 +318,7 @@
318 318  |(% style="width:399px" %)**Declining Purchase Amount**|(% style="width:370px" %)Customer’s purchase amount decreased by X% over a period|(% style="width:446px" %)If purchase amount drops by X%, send an email with personalized product recommendations.
319 319  |(% style="width:399px" %)[[**Declining Website Visits**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41F44043E44843B43E43443D43543944143F43E44143B43543443D43543343E43F43E44143544943543D43844F441430439442430"]]|(% style="width:370px" %)Customer hasn’t visited the site|(% style="width:446px" %)Send an email if the customer hasn’t visited the site in X days.
320 320  
321 -==== 3. Use Cases for the **Personalized Product Recommendations** Algorithm in Website Personalization ====
311 +==== 3. Use cases for the Personalized Product Recommendations algorithm in website personalization ====
322 322  
323 323  (% class="table-bordered" style="width:1227px" %)
324 324  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:369px" %)**Trigger**|(% style="text-align:center; width:452px" %)**Use Case**
... ... @@ -327,7 +327,7 @@
327 327  |(% style="width:399px" %)[[**Website Login**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41A43B43843543D44243F43E44143544243843B441430439442"]]|(% style="width:369px" %)Authorized customer login|(% style="width:452px" %)**You Might Like** or **Just for You** section.
328 328  Recommendations related to the viewed product.
329 329  
330 -==== 4. General Concept of the **Personalized Product Recommendations** Algorithm ====
320 +==== 4. General concept of the Personalized Product Recommendations algorithm ====
331 331  
332 332  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.
333 333  Using this method within an ensemble of models—alongside other algorithms—produces more relevant product recommendations for each customer.
Created 2025/10/22 03:38
Last edited 2026/03/25 10:03

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