AI Recommendation Engine

AI Recommendation Engine: Smart, Personalized Guidance, no more blind guessing.

This feature combines natural language processing (NLP) with machine learning to understand a user’s needs, level, and preferences through conversation. Unlike traditional static, menu-based recommendations, the AI engine adapts dynamically—it builds on each response, asking follow-up questions and refining suggestions in real time. The result is guidance that feels personal, relevant, and practical. This interactive approach not only enhances the user experience but also significantly boosts conversion rates.

On the homepage, select AI Recommendation Engine from the application menu ⬇️

Once inside, go to Basic Settings ⬇️

  • Keywords to enter/exit the AI Recommendation Engine

  • Message displayed upon entry into the AI Recommendation Engine

  • Content displayed upon exit from the AI Recommendation Engine

Next, select language model and the language

Model selection usually represents different versions, capabilities, and application scenarios. The choice of model can directly impact the quality of generated content, its language comprehension, creativity, and computational efficiency.

After completing the settings, move on to Item Management—found under the Database section in the side toolbar ⬇️

First, add an item

Type in the item name, item URL, item description, and upload an image

Purpose of Adding Category Tags

The goal of setting up category tags is to improve both the accuracy and efficiency of user preference analysis. With these tags in place, the system can analyze behaviors and identify preferences based on their attributes, enabling more precise surveys and targeted communication for different user groups

Content Hashtag This feature automatically creates relevant hashtags based on product information, helping users quickly generate keywords when they’re unsure which tags to use. The result boosts both content visibility and searchability. By clicking the “Generate Content Hashtag” button, the system intelligently analyzes product attributes and suggests recommended tags. If the auto-generated hashtags don’t fully meet your needs, you can manually edit and adjust them to better match your brand voice and marketing goals.

Next, we go to “generate content hashtag”

When a user selects “#Generate Content Hashtag” within an item and clicks “Confirm,” the system automatically generates related hashtags based on the product information. These auto-generated tags are displayed in gray to distinguish them from manually edited tags.

In Item Management, there’s also a “#Generate Content Hashtag” button at the top right. When clicked, the system automatically generates relevant hashtags based on all product information, applying them across every existing item in the database.

Step 2: Preference Survey ⬇️

During the preference survey, administrators can design guided questions—such as “Question 1,” “Question 2,” “Question 3”—based on product attributes and usage scenarios. These questions help consumers quickly narrow down suitable items according to their needs and preferences. Under each question, you can add and configure multiple answer choices through the “Add Option” feature, allowing the system to recommend corresponding items automatically based on user responses.

⬆️ If administrators have not yet defined specific survey questions, the system also provides an “AI-Generated Questions and Answers” feature. Using semantic analysis and product attribute recognition, it automatically creates guided questions along with corresponding options—helping teams quickly build an effective, structured survey flow.

After the system generates product recommendations, administrators can further refine how each item is displayed. This can be done by clicking “Edit” on the “View Item” page.

The system will then show the available product details—such as summary, price, discount price, size, or brand—in the “Recommendation Preview” section. Any fields left blank will not appear in the preview; only the information you’ve entered will be displayed.

Finally, go to “Recommendation Preview Settings” and check the fields you want to display. Once selected, your recommendation display setup is complete.

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