Loymax, 2026
From version 17.1
edited by Anastasia Zanina
on 2026/03/25 10:03
Change comment: Renamed back-links.
To version 1.3
edited by Andrej Rylov
on 2025/10/22 07:41
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Summary

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Parent
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1 -Main.Loymax_AI.WebHome
1 +Main.General_information.Loymax_Loyalty.Recommendation_systems.WebHome
Author
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1 -XWiki.zanina
1 +XWiki.arylov
Content
... ... @@ -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  
... ... @@ -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.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 [[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.
20 20  
21 21  Business goals achieved through personalized communications using product recommendations:
22 22  
... ... @@ -34,30 +34,59 @@
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.Usage.Smart_Communications.WebHome]], please contact Loymax 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 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  
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="Scheme_module_interaction.png" width="1200"/}}
46 46  
47 47  Currently, 5 algorithms are available for generating product recommendations:
48 48  
49 -* [[Popular Products>>doc:||anchor="01"]]
50 -* [[Popular Products in Category>>doc:||anchor="02"]]
51 -* [[Bought Together>>doc:||anchor="03"]]
52 -* [[Similar Products>>doc:||anchor="04"]]
53 -* [[Personalized Product Recommendations>>doc:||anchor="05"]]
49 +(% class="table-bordered table-sticky" %)
50 +(% class="info" %)|(% style="text-align:center; width:58px" %)**No.**|(% style="text-align:center; width:409px" %)**Name**|(% style="text-align:center; width:495px" %)**Metavariable/macro for use in [[Smart Communications campaigns>>doc:Main.Usage.Smart_Communications.Mass_mailings.WebHome]]**|(% style="text-align:center; width:477px" %)**Example of macro output in final message (shown for email)**
51 +|(% style="width:58px" %)1.|(% style="width:409px" %)**[[Popular Products>>doc:||anchor="01"]]**|(% style="width:495px" %){% set clientRecommendation = client.recommendations('**Popular**') %}
52 +{% for recommendation in clientRecommendation~|get(3) %}
53 +<br>~{~{recommendation.name}} 
54 +{% endfor %}|(% style="width:477px" %)(((
55 +Product Name 1
56 +Product Name 2
57 +Product Name 3
58 +)))
59 +|(% style="width:58px" %)2.|(% style="width:409px" %)**[[Popular Products in Category>>doc:||anchor="02"]]**|(% style="width:495px" %){% set clientRecommendation = client.recommendations('**PopularsCategory**') %}
60 +{% for recommendation in clientRecommendation~|get(3) %}
61 +<br>~{~{recommendation.name}} 
62 +{% endfor %}|(% style="width:477px" %)Product Name 1
63 +Product Name 2
64 +Product Name 3
65 +|(% style="width:58px" %)3.|(% style="width:409px" %)**[[Bought Together>>doc:||anchor="03"]]**|(% style="width:495px" %){% set clientRecommendation = client.recommendations('**BoughtTogether**') %}
66 +{% for recommendation in clientRecommendation~|get(3) %}
67 +<br>~{~{recommendation.name}} 
68 +{% endfor %}|(% style="width:477px" %)Product Name 1
69 +Product Name 2
70 +Product Name 3
71 +|(% style="width:58px" %)4.|(% style="width:409px" %)**[[Similar Products>>doc:||anchor="04"]]**|(% style="width:495px" %){% set clientRecommendation = client.recommendations('**SimilarProducts**') %}
72 +{% for recommendation in clientRecommendation~|get(3) %}
73 +<br>~{~{recommendation.name}} 
74 +{% endfor %}|(% style="width:477px" %)Product Name 1
75 +Product Name 2
76 +Product Name 3
77 +|(% style="width:58px" %)5.|(% style="width:409px" %)**[[Personalized Product Recommendations>>doc:||anchor="05"]]**|(% style="width:495px" %){% set clientRecommendation = client.recommendations('**RelatedPurchases**') %}
78 +{% for recommendation in clientRecommendation~|get(3) %}
79 +<br>~{~{recommendation.name}} 
80 +{% endfor %}|(% style="width:477px" %)Product Name 1
81 +Product Name 2
82 +Product Name 3
54 54  
55 55  (% class="box infomessage" %)
56 56  (((
57 57  **Notes:**
58 58  
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.
88 +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:.Control_structure.WebHome]].
89 +1. API methods for integration into client-facing services ([[Mobile Application>>doc:Main.General_information.Additional_services.Mobile_application.WebHome]], [[Personal Account>>doc:Main.General_information.Additional_services.Personal_account.WebHome]]) are provided upon separate request to Loymax 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:
... ... @@ -75,9 +75,9 @@
75 75  1. Mobile Application,
76 76  1. Personal Account.
77 77  
78 -=== **{{id name="01"/}}**Popular Products** algorithm** ===
107 +=== **{{id name="01"/}}“Popular Products” Algorithm** ===
79 79  
80 -==== 1. Description of the Popular Products algorithm ====
109 +==== 1. Description of the **Popular Products** Algorithm ====
81 81  
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.
... ... @@ -84,46 +84,48 @@
84 84  Product rankings are calculated based on total units sold across all customer purchases—i.e., the most purchased items.
85 85  **All purchases** include both online and offline transactions processed through Loymax.
86 86  
87 -==== 2. Use cases for the Popular Products algorithm in personalized campaigns ====
116 +==== 2. Use Cases for the **Popular Products** Algorithm in Personalized Campaigns ====
88 88  
118 +**Email Campaigns:**
119 +
89 89  (% class="table-bordered" style="width:1218px" %)
90 90  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:370px" %)**Trigger**|(% style="text-align:center; width:446px" %)**Use Case**
91 -|(% style="width:399px" %)[[Abandoned View>>doc:Main.Smart_Communications.SMC_Use.Campaigns.Triggers.WebHome||anchor="abandonedview"]]|(% style="width:370px" %)Product viewed but not purchased|(% style="width:446px" %)(((
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" %)(((
92 92  Customer viewed a product page and then left the site.
93 93  
94 94  After a defined time interval (t), send an email featuring the most popular products.
95 95  )))
96 -|(% style="width:399px" %)[[Abandoned Category View>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="abandonedcatview"]]|(% style="width:370px" %)Category viewed but nothing purchased|(% style="width:446px" %)(((
127 +|(% style="width:399px" %)[[**Abandoned Category View**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41144043E44843543D43D44B43943F44043E44143C43E44244043A43044243543343E440438438"]]|(% style="width:370px" %)Category viewed but nothing purchased|(% style="width:446px" %)(((
97 97  Customer browsed a product category and then left the site.
98 98  
99 99  After a defined time interval (t), send an email featuring the most popular products.
100 100  )))
101 -|(% style="width:399px" %)[[Abandoned Cart>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="abandonedcart"]]|(% style="width:370px" %)Product added to cart but not purchased|(% style="width:446px" %)(((
132 +|(% 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" %)(((
102 102  Customer added a product to the cart and then left the site.
103 103  
104 104  After a defined time interval (t), send a reminder email about the abandoned cart along with the most popular products.
105 105  )))
106 106  
107 -==== (% 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(%%) ====
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
108 108  
109 109  (% class="table-bordered" style="width:1218px" %)
110 110  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:370px" %)**Trigger**|(% style="text-align:center; width:446px" %)**Use Case**
111 -|(% style="width:399px" %)[[Website Homepage>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="site_visit"]]|(% style="width:370px" %)Website visit|(% style="width:446px" %)**Popular Products** section.
142 +|(% style="width:399px" %)[[**Website Homepage**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41A43B43843543D44243F43E44143544243843B441430439442"]]|(% style="width:370px" %)Website visit|(% style="width:446px" %)**Popular Products** section.
112 112  Customer is on the homepage. The most popular products are displayed.
113 -|(% style="width:399px" %)[[Current Product Page View>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="product_view"]]|(% style="width:370px" %)Product view|(% style="width:446px" %)**Popular Products** section.
144 +|(% style="width:399px" %)[[**Current Product Page View**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41A43B43843543D44243F43E44143C43E44244043543B43F44043E43444343A442A043D430441430439442435"]]|(% style="width:370px" %)Product view|(% style="width:446px" %)**Popular Products** section.
114 114  Customer is viewing a product. The most popular products are displayed.
115 -|(% style="width:399px" %)[[Current Category Page View>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="cat_view"]]|(% style="width:370px" %)Category view|(% style="width:446px" %)**Popular Products** section.
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.
116 116  Customer is browsing a category. The most popular products are displayed.
117 117  
118 -==== 4. General concept of the Popular Products algorithm ====
149 +==== 4. General Concept of the **Popular Products** Algorithm ====
119 119  
120 120  **Example**: Generating recommendations by selecting the top 1 most popular product from each category.
121 121  
122 122  |(% style="border-color:#ffffff; text-align:center" %)[[image:attach:Recommendations_01.png]]
123 123  
124 -=== **{{id name="02"/}}Popular Products in Category algorithm** ===
155 +=== **{{id name="02"/}}“Popular Products in Category” Algorithm** ===
125 125  
126 -==== 1. Description of the Popular Products in Category algorithm ====
157 +==== 1. Description of the **Popular Products in Category** Algorithm ====
127 127  
128 128  The **Popular Products in Category** algorithm calculates a popularity ranking among products within each specific category.
129 129  
... ... @@ -132,27 +132,29 @@
132 132  Product rankings are calculated based on total units sold across all customer purchases—i.e., the most purchased items.
133 133  **All purchases** include both online and offline transactions processed through Loymax.
134 134  
135 -==== 2. Use cases for the Popular Products in Category algorithm in personalized campaigns ====
166 +==== 2. Use Cases for the **Popular Products in Category** Algorithm in Personalized Campaigns ====
136 136  
137 137  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.
138 138  
139 139  The category/classifier level assigned to a product can be either the lowest or a higher level in the hierarchy.
140 140  
172 +**Email Campaigns:**
173 +
141 141  (% class="table-bordered" style="width:1218px" %)
142 142  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:370px" %)**Trigger**|(% style="text-align:center; width:446px" %)**Use Case**
143 -|(% style="width:399px" %)[[Abandoned View>>doc:Main.Smart_Communications.SMC_Use.Campaigns.Triggers.WebHome||anchor="abandonedview"]]|(% 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.
144 -|(% style="width:399px" %)[[Abandoned Cart>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="abandonedcart"]]|(% 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.
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 +|(% 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.
145 145  
146 -==== 3. Use cases for the Popular Products in Category algorithm in website personalization ====
179 +==== 3. Use Cases for the **Popular Products in Category** Algorithm in Website Personalization ====
147 147  
148 148  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.
149 149  
150 150  (% class="table-bordered" style="width:1218px" %)
151 151  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:369px" %)**Trigger**|(% style="text-align:center; width:447px" %)**Use Case**
152 -|(% style="width:399px" %)[[Current Product Page View>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="product_view"]]|(% style="width:369px" %)Product view|(% style="width:447px" %)**Compared With This Product** section.
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.
153 153  Recommend popular products from the same category based on the currently viewed item.
154 154  
155 -==== 4. General concept of the algorithm ====
188 +==== 4. General Concept of the Algorithm ====
156 156  
157 157  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.
158 158  
... ... @@ -162,29 +162,31 @@
162 162  
163 163  |(% style="border-color:#ffffff; text-align:center" %)[[image:attach:Recom_02.png]]
164 164  
165 -=== **{{id name="03"/}}Similar Products algorithm** ===
198 +=== **{{id name="03"/}}“Similar Products” Algorithm** ===
166 166  
167 -==== 1. Description of the Similar Products algorithm ====
200 +==== 1. Description of the **Similar Products** Algorithm ====
168 168  
169 169  The **Similar Products** algorithm recommends alternative [[products*>>doc:||anchor="Star"]] that are the most similar to the viewed/ordered/purchased item.
170 170  
171 171  It recommends N products that have similar attributes to the viewed/ordered/purchased product.
172 172  
173 -==== 2. Use cases for the Similar Products algorithm in personalized campaigns ====
206 +==== 2. Use Cases for the **Similar Products** Algorithm in Personalized Campaigns ====
174 174  
208 +**Email Campaigns:**
209 +
175 175  (% class="table-bordered" style="width:1218px" %)
176 176  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:370px" %)**Trigger**|(% style="text-align:center; width:446px" %)**Use Case**
177 -|(% style="width:399px" %)[[Abandoned View>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="abandonedview"]]|(% 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.
178 -|(% style="width:399px" %)[[Abandoned Cart>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="abandonedcart"]]|(% 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.
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 +|(% 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.
179 179  
180 -==== 3. Use cases for the Similar Products algorithm in website personalization ====
215 +==== 3. Use Cases for the **Similar Products** Algorithm in Website Personalization ====
181 181  
182 182  (% class="table-bordered" style="width:1218px" %)
183 183  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:369px" %)**Trigger**|(% style="text-align:center; width:447px" %)**Use Case**
184 -|(% style="width:399px" %)[[Current Product Page View>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="product_view"]]|(% style="width:369px" %)Product view|(% style="width:447px" %)**You Might Like** section.
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.
185 185  Recommend alternative products similar to the one currently viewed by the customer.
186 186  
187 -==== 4. General concept of the algorithm ====
222 +==== 4. General Concept of the Algorithm ====
188 188  
189 189  |(% style="border-color:white; width:368px" %)[[image:attach:ARL_rules_example.gif]]|(% style="border-color:white; width:1070px" %)(((
190 190  For each product, a neural network generates an N-dimensional vector representation (embedding) based on product descriptions, attributes, purchase/view statistics/content.
... ... @@ -196,32 +196,34 @@
196 196  Similar products are selected based on minimal cosine distance between vectors—i.e., those sharing the most similar combination of product attributes.
197 197  )))
198 198  
199 -=== **{{id name="04"/}}Bought Together algorithm** ===
234 +=== **{{id name="04"/}}“Bought Together” Algorithm** ===
200 200  
201 -==== 1. Description of the Bought Together algorithm ====
236 +==== 1. Description of the **Bought Together** Algorithm ====
202 202  
203 203  The **Bought Together** algorithm recommends products that most frequently appear in the same transaction as the viewed/ordered/purchased items.
204 204  
205 -==== 2. Use cases for the Bought Together algorithm in personalized campaigns ====
240 +==== 2. Use Cases for the **Bought Together** Algorithm in Personalized Campaigns ====
206 206  
242 +**Email Campaigns:**
243 +
207 207  (% class="table-bordered" style="width:1218px" %)
208 208  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:370px" %)**Trigger**|(% style="text-align:center; width:446px" %)**Use Case**
209 -|(% style="width:399px" %)[[Abandoned View>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="abandonedview"]]|(% 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.
210 -|(% style="width:399px" %)[[Abandoned Cart>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="abandonedcart"]]|(% 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.
211 -|(% style="width:399px" %)[[Thank You for Your Purchase!>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="order_complete"]]|(% 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.
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 +|(% 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 +|(% 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.
212 212  
213 -==== 3. Use cases for the Bought Together algorithm in website personalization ====
250 +==== 3. Use Cases for the **Bought Together** Algorithm in Website Personalization ====
214 214  
215 215  (% class="table-bordered" style="width:1218px" %)
216 216  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:369px" %)**Trigger**|(% style="text-align:center; width:447px" %)**Use Case**
217 -|(% style="width:399px" %)[[Current Product Page View>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="product_view"]]|(% style="width:369px" %)Product view|(% style="width:447px" %)**Frequently Bought Together** section.
254 +|(% 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" %)**Frequently Bought Together** section.
218 218  Recommend complementary products.
219 -|(% style="width:399px" %)[[Product Added to Cart>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="container_add"]]|(% style="width:369px" %)Product added to cart|(% style="width:447px" %)**Frequently Bought Together** section.
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.
220 220  Recommend complementary products.
221 221  
222 -==== 4. General concept of the Bought Together algorithm ====
259 +==== 4. General Concept of the **Bought Together** Algorithm ====
223 223  
224 -This algorithm incorporates a series of models and techniques based on the [[Association Rule Learning>>https://en.wikipedia.org/wiki/Association_rule_learning]] methodology.
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.
225 225  
226 226  Association rules for product recommendations consist of logical rules that identify relationships between products and generate recommendations based on those relationships. These rules help predict which products may interest a customer and offer relevant suggestions based on prior purchases or current browsing behavior, thereby enhancing personalization and customer satisfaction.
227 227  
... ... @@ -254,9 +254,9 @@
254 254  * Pears to a customer who purchased Brie cheese (Rule 3).
255 255  )))
256 256  
257 -=== **{{id name="05"/}}Personalized Product Recommendations algorithm** ===
294 +=== **{{id name="05"/}}“Personalized Product Recommendations” Algorithm** ===
258 258  
259 -==== 1. Description of the Personalized Product Recommendations algorithm ====
296 +==== 1. Description of the **Personalized Product Recommendations** Algorithm ====
260 260  
261 261  (% class="wikigeneratedid" id="H" %)
262 262  The **Personalized Product Recommendations** algorithm generates product suggestions based on an individual customer’s behavior (Customer ID) and the behavior of similar customers.
... ... @@ -265,35 +265,37 @@
265 265  (% class="wikigeneratedid" %)
266 266  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.
267 267  
268 -==== 2. Use cases for the Personalized Product Recommendations algorithm in personalized campaigns ====
305 +==== 2. Use Cases for the **Personalized Product Recommendations** Algorithm in Personalized Campaigns ====
269 269  
307 +**Email Campaigns:**
308 +
270 270  (% class="table-bordered" style="width:1218px" %)
271 271  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:370px" %)**Trigger**|(% style="text-align:center; width:446px" %)**Use Case**
272 272  |(% style="width:399px" %)(((
273 -[[Abandoned View>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="abandonedview"]]
312 +[[**Abandoned View**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41144043E44843543D43D44B43943F44043E44143C43E442440"]]
274 274  
275 275  
276 276  )))|(% style="width:370px" %)Product viewed but not ordered|(% style="width:446px" %)Send an email X hours after viewing.
277 -|(% style="width:399px" %)[[Abandoned Cart>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="abandonedcart"]]|(% style="width:370px" %)Product added to cart but not purchased|(% style="width:446px" %)Send an email X days after adding to cart.
278 -|(% style="width:399px" %)**Purchase Recommendations**|(% style="width:370px" %)[[Customer made a purchase>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="order_complete"]]|(% style="width:446px" %)Send an email X days after purchase.
316 +|(% 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 X days after adding to cart.
317 +|(% style="width:399px" %)**Purchase Recommendations**|(% style="width:370px" %)[[Customer made a purchase>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H42143443543B43043D43743043A430437"]]|(% style="width:446px" %)Send an email X days after purchase.
279 279  |(% 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.
280 -|(% style="width:399px" %)[[Declining Website Visits>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="time_since_visit"]]|(% 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.
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.
281 281  
282 -==== 3. Use cases for the Personalized Product Recommendations algorithm in website personalization ====
321 +==== 3. Use Cases for the **Personalized Product Recommendations** Algorithm in Website Personalization ====
283 283  
284 284  (% class="table-bordered" style="width:1227px" %)
285 285  (% class="info" %)|(% style="text-align:center; width:399px" %)**Campaign**|(% style="text-align:center; width:369px" %)**Trigger**|(% style="text-align:center; width:452px" %)**Use Case**
286 -|(% style="width:399px" %)[[Current Product Page View>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="product_view"]]|(% style="width:369px" %)Product page view|(% style="width:452px" %)**You Might Like** or **Just for You** section.
325 +|(% style="width:399px" %)[[**Current Product Page View**>>doc:Main.Usage.Smart_Communications.Campaign_list.Triggers.WebHome||anchor="H41A43B43843543D44243F43E44143C43E44244043543B43F44043E43444343A442A043D430441430439442435"]]|(% style="width:369px" %)Product page view|(% style="width:452px" %)**You Might Like** or **Just for You** section.
287 287  Recommendations related to the viewed product.
288 -|(% style="width:399px" %)[[Website Login>>doc:Main.Usage.Smart_Communications.Campaigns.Triggers.WebHome||anchor="site_visit"]]|(% style="width:369px" %)Authorized customer login|(% style="width:452px" %)**You Might Like** or **Just for You** section.
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.
289 289  Recommendations related to the viewed product.
290 290  
291 -==== 4. General concept of the Personalized Product Recommendations algorithm ====
330 +==== 4. General Concept of the **Personalized Product Recommendations** Algorithm ====
292 292  
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.
335 +**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.
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  
... ... @@ -306,10 +306,10 @@
306 306  (((
307 307  **See also:**
308 308  
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]]
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"]]
348 +* [[Product Recommendations Module Control Structure>>doc:.Control_structure.WebHome]]
349 +* [[Recommendation System Integration>>doc:Main.General_information.Loymax_Loyalty.recommendation_systems.WebHome]]
350 +* [[Omnichannel>>doc:Main.General_information.Sale_channels.WebHome]]
351 +* [[Attributes Related to Loymax AI>>doc:Main.Usage.MMP.Admin_panel.Client_attributes.Common_attributes.WebHome||anchor="ML"]]
313 313  * [[Personal Offers Using Machine Learning Mechanics>>doc:Main.Installation_and_configuration.Extra_modules.CommunicationService_ML.WebHome]]
314 314  )))
315 315  
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Created 2025/10/22 03:38
Last edited 2026/03/25 10:03

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