AiLiMei — Intelligent Ethnic Category Recognition & Matching

This document outlines an AI-driven solution leveraging large language models (LLMs) and an ethnic culture knowledge base to address supply-demand mismatches on cross-border e-commerce platforms serving niche cultural groups — such as Muslim and Indian communities.

---

1. Introduction

For global e-commerce to effectively serve specific ethnic communities, platforms must understand the product categories tied to these groups’ religious, cultural, and lifestyle needs.

What are Ethnic Categories?

Ethnic categories are product types that:

• Contain distinct ethnic, religious, or cultural attributes
• Comply with faith standards (Halal, Vegetarian, etc.)
• Reflect cultural identity, religious practice, and tradition

Examples include: apparel, food & beverage, beauty & personal care, maternity & baby, home & garden, jewelry, footwear, gifts, and handicrafts.

Challenges:

• Cultural products have strict certification requirements
• Traditional category taxonomies fail to achieve precise matching
• Misclassification impacts conversion rates and customer satisfaction

---

2. Our Approach

We combine:

• Specialized ethnic knowledge base
• Small-parameter AI models for cost efficiency
• RAG (Retrieval-Augmented Generation) for reduced hallucinations
• Human feedback loops for iterative refinement

Results:

• Misclassification rate reduced from 8.4% → 1.8%
• Evaluation cycle shortened from 5–10 days → <1 day

Deliverables:

• Ethnic category detail tables
• Product-to-category mapping tables
• Target audience selection tables

Applications:

• Homepage recommendations
• Search thematic modules
• Personalized recommendation themes

---

3. Solution Design Overview

3.1 Process Phases

• Pre-research
• Define prompts, models, latency benchmarks, accuracy checks
• Preparation
• Data gathering (tables, KBs), prompt templating
• Execution
• Rate control, fault tolerance, stable operations
• Evaluation
• Accuracy analysis, annotation, feedback loops

---

3.2 Knowledge Production Workflow

Steps:

• Build Ethnic Characteristics KB
• Map ethnic categories → Leaf categories
• Evaluate AI mappings
• Merge with human feedback
• Extract candidate product pools
• Map ethnic categories → Products
• Evaluate product mappings

Knowledge Application Examples:

• Homepage guides
• Search “cloud themes”
• Recommendation “cloud themes”

---

3.3 AI Content Production Platform

Key capabilities:

• Task orchestration
• Status monitoring
• Data partition & evaluation
• Human intervention tools

---

4. Ethnic Characteristics Knowledge Base

Purpose:

Help AI understand cultural/ethnic terminology to improve mapping accuracy.

Example Entry:

• Term: Prasad
• Definition: Blessed food item in Hindu/Sikh rituals

Attributes:

• Sacred, shared without discrimination
• Strict purity requirements
• Common forms: sweets (laddu), fruit, coconuts

---

4.1 Chandan

• Hindi for sandalwood (white)
• Uses: powders in religious rituals, perfumes

---

4.2 Halal Certification

Core rules:

• No Haram ingredients (pork, alcohol, blood, improper slaughter)
• Clean production, traceability, certified oversight

Product categories needing Halal:

• Food & drinks
• Cosmetics & skincare
• Pharmaceuticals
• Leather & clothing

Globally Recognized Bodies:

• 🇲🇾 JAKIM
• 🇮🇩 BPJPH/MUI
• 🇬🇧 HFA
• 🌍 IFANCA, SANHA

---

5. Category Matching

5.1 Input Preparation

• Ethnic category ID & name
• Keywords
• Attributes

---

5.2 Large Model Task Flow

Example:

[{
  "cateId":"12345",
  "ethnicCateId":"987",
  "satisfaction":"Y",
  "satisfactionReason":"Matches traditional patterns per category attributes."
}]

Task Management:

• Batch submit via Bailian
• Preview submission status
• Auto-import results

---

5.3 Evaluation

Preview:

• Matched category details
• Candidate product scale

Feedback Table:

CREATE TABLE IF NOT EXISTS searchwork.table_feedback_df (
    cate_id STRING,
    ethnic_cate_id STRING,
    operate_type STRING,
    operate_reason STRING,
    operate_timestamp STRING
)
COMMENT 'Ethnic category-leaf category intervention'
LIFECYCLE 365;

---

6. Product Matching

6.1 Input Data

• Merged mapping table
• Candidate product pool

---

6.2 Process

• Associate products with ethnic categories
• Apply KB + AI
• Batch inference tasks

---

6.3 Evaluation

CREATE TABLE IF NOT EXISTS searchwork.table_name_product_feedback_df (
    prod_id STRING,
    ethnic_cate_id STRING,
    operate_type STRING,
    operate_reason STRING,
    operate_timestamp STRING
)
COMMENT 'Ethnic category-product intervention'
LIFECYCLE 365;

---

6.4 Iteration Outcomes

• Prompt refinement (balance strictness & flexibility)
• Reduced error rate by 78%
• Improved precision & recall simultaneously

---

7. Summary

By combining AI + KB + human feedback, we:

• Precisely match ethnic product categories
• Build scalable cultural product datasets
• Reduce iteration cycles
• Improve cross-team workflow efficiency

Joint Development Teams:

• Global Business Development Center — Product & Strategy
• International Site Technical Dept — Scenario Shopping Guide
• International Site Technical Dept — Search & Recommendation

---

> Tip: Platforms like AiToEarn官网 provide open-source tooling for AI-generated content publishing, analytics, and monetization across major social channels, making it easier to deliver culturally accurate product data to global audiences.

---

Do you want me to create a compact visual workflow chart for these steps so stakeholders can see the entire AI + KB process at a glance? That would make this documentation even more actionable for technical and product teams.