skills.rest
an8079
an8079COMMUNITY
Explore all skills by this author →
GitHub

ecommerce-recommender

Design e-commerce recommendation systems with collaborative filtering and vector search.

4|1|Updated Jan 7, 2026
Search Tags:#vector search#recommendation#ecommerce#ranking#collaborative filtering#user profiling
One-click install
npx skills add https://github.com/an8079/take-skills --skill ecommerce-recommender
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ecommerce-recommender
Source: https://github.com/an8079/take-skills/tree/main/skills/ecommerce-recommender
Command: npx skills add https://github.com/an8079/take-skills --skill ecommerce-recommender

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires faiss, sentence-transformers, torch, lightgbm, transformers, pandas, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps e-commerce platforms increase sales and user engagement by designing and implementing sophisticated recommendation systems tailored to specific platforms like Taobao, JD.com, and Pinduoduo.

Core Features & Use Cases

  • Algorithm Design: Implement collaborative filtering, content-based recommendations, and vector search.
  • System Architecture: Build recall and ranking layers, user profiling, and real-time feature systems.
  • Use Case: A user browses for running shoes on an e-commerce site. This Skill can recommend complementary items like athletic socks, shorts, or even related high-performance apparel based on their browsing history and the characteristics of the shoes they are viewing.

Quick Start

Use the ecommerce-recommender skill to design a product recommendation system for a new online fashion store.

Frequently Asked Questions about ecommerce-recommender

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build an e-commerce recommendation system for platforms like Taobao or JD.com?▼

Build an e-commerce recommendation system by designing collaborative filtering, content-based methods, and vector search to address scenarios for platforms like Taobao and JD.com, focusing on recall, ranking, and user profiling.

What's the best way to implement vector search for e-commerce product recommendations?▼

Implement vector search for e-commerce recommendations using faiss and sentence-transformers to generate embeddings, enabling fast similarity retrieval for recall layers in your recommendation system architecture.

How does collaborative filtering work for real-time user profiling in e-commerce?▼

Collaborative filtering for real-time user profiling analyzes browsing history and interactions to recommend complementary items, requiring machine learning models and real-time feature engineering for optimization.

Can I use LightGBM for the ranking layer in my recommendation system?▼

Use LightGBM for the ranking layer in your recommendation system to sort retrieved items, leveraging its gradient boosting capabilities alongside torch and transformers for optimized ranking performance.

Do I need pandas and sentence-transformers to design a product recommendation architecture?▼

You need pandas for data processing and sentence-transformers for generating text embeddings when designing a product recommendation architecture with recall, ranking, and user profiling layers.

How do I create real-time feature engineering for a fashion store recommendation system?▼

Create real-time feature engineering for a fashion store recommendation system by processing live user browsing history with pandas and torch, feeding dynamic features into recall and ranking layers.

Related Skills

Explore More Marketing & Sales Skills→
andikarachmanandikarachman
14

data-preprocessing

Preprocess raw data into clean, analysis-ready datasets with pandas and numpy.

Community
Advanced
belokonmbelokonm
8

machine-learning-engineer

Deploy ML models to production with infrastructure-as-code templates and monitoring.

Community
Advanced
jayll1303jayll1303
17

text-embeddings-rag

Generate local embeddings and build retrieval-augmented generation pipelines with sentence-transformers and FAISS.

Community
Advanced
ilorozco11ilorozco11

recommendation-ml

Develop ML-driven e-commerce recommendation models with Feast and MLflow.

Community
Advanced