Product Recommendation System is a machine learning-based project that provides personalized product recommendations to users based on their interaction history, similar users, and also the popularity of products.

商品关联关系挖掘，使用Spring Boot开发框架和Spark MLlib机器学习框架，通过FP-Growth算法，分析用户的购物车商品数据，挖掘商品之间的关联关系。项目对外提供RESTFul接口。

Analyse customer segmentation, sentiment on product review, and built a product recommender system

An image recognition model which is capable of identifying the pattern on a dress image

This project is an advanced implementation of a product recommendation system that leverages the power of Sentence Transformers.

I have improved the demo by using Azure OpenAI’s Embedding model (text-embedding-ada-002), which has a powerful word embedding capability. This model can also vectorize product key phrases and recommend products based on cosine similarity, but with better results. You can find the updated repo here.

An item-based recommender model that computes cosine similarity for each item pairs using the item factors matrix generated by Spark MLlib’s ALS algorithm and recommends top 5 items based on the selected item.

This is a code sample repository for online retail product recommendations using Collaborative Filtering (Memory-Based, aka History-Based). The source data used the famous Online Retail Data Set from UCI Machine Learning Repository.

Flask app for a collaborative product recommendation engine that uses Louvain clustering.

E-Commerce web application based on Django framework

A project for the subject "New uses of Computing Science" at Universitat de Barcelona

Image embedding in Java

Product Recommendation System using Machine Learning

GlancyAI is an LLM (like ChatGPT) that you can talk with, and it recommends products and helps you make your educated guess to buy a product.

(FATEC PI) - Ecommerce integrado com aprendizagem de máquina para recomendar produtos aos clientes

A hybrid recommendation system combining cold-start collaborative filtering with online updates and persistent queues. It adapts to new browsing data in real time, filtering out purchased products to ensure fresh, personalized recommendations.

Using algorithms such as collaborative filtering, content-based filtering, or hybrid methods, this recommendation engine offers personalized suggestions to users, enhancing their shopping or browsing experience.

The sample code repository leverages Azure Text Analytics to extract key phrases from the product description as additional product features. And perform text relationship analysis with TF-IDF vectorization and Cosine Similarity for product recommendation.

ProdSense: Multi-agent product recommendation system powered by CrewAI. Scrapes product details from any product website, discovers alternatives, analyzes community sentiments, and curates YouTube reviews. Outputs tailored Markdown recommendations. Experimental, API-driven, and open for tinkering.