Using the MovieLens dataset, will create a customer recommendation system from scratch using PyTorch.
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Updated
May 30, 2024 - Jupyter Notebook
Using the MovieLens dataset, will create a customer recommendation system from scratch using PyTorch.
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.
E-Commerce web application based on Django framework
Use my Docker image for demo purpose
This project is an advanced implementation of a product recommendation system that leverages the power of Sentence Transformers.
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.
Personalized recommender system for Sephora's cosmetics e-commerce platform. Using content-based filtering, with TF-IDF Vectorizer to extract product features and cosine similarity to recommend similar items based on user preferences. And collaborative filtering with SVD for identifying user patterns and recommending highly-rated products.
Clothing Chain Sales Analysis to analyze the sales by various factors, customer ratings and product recommendation analysis
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.
Robust product recommendations using topological data analysis. 4-week project completed during Insight Fellows Program, AI Silicon Valley 2020 B Cohort
This is a demo repo demonstrating how to perform Market Basket Analysis (MBA) with a Retail (Grocery Store) sample.
Product Recommendation System using Machine Learning
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 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.
Recommender system finds its application in many aspects of the online ecosystem including product recommendation, movie recommendation, books, news, video recommendation to name a few.
E-Commerce web application based on Django framework.
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。
Various AI Chatbots build with Rasa Framework
Flask app for a collaborative product recommendation engine that uses Louvain clustering.
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