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。
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Updated
Jun 7, 2021 - Java
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。
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.
Analyse customer segmentation, sentiment on product review, and built a product recommender system
Flask app for a collaborative product recommendation engine that uses Louvain clustering.
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.
Product Recommendation System using Machine Learning
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.
Image embedding in Java
Various AI Chatbots build with Rasa Framework
An image recognition model which is capable of identifying the pattern on a dress image
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.
This project is an advanced implementation of a product recommendation system that leverages the power of Sentence Transformers.
E-Commerce web application based on Django framework
Use my Docker image for demo purpose
Product Recommendation Engine
Deep Learning for Computer Vision in Java
E-Commerce web application based on Django framework.
A project for the subject "New uses of Computing Science" at Universitat de Barcelona
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