70 results for “topic:product-recommendation”
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
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。
No description provided.
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.
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.
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.
一款开箱即用的 AI 智能客服系统，人工客服，客户转化
E-Commerce web application based on Django framework
Flask app for a collaborative product recommendation engine that uses Louvain clustering.
Image embedding in Java
A project for the subject "New uses of Computing Science" at Universitat de Barcelona
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.
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.
(FATEC PI) - Ecommerce integrado com aprendizagem de máquina para recomendar produtos aos clientes
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.
Product Recommendation System using Machine Learning
A hands-on recommendation project exploring baseline ranking methods and an ARL-inspired policy for improving item suggestions. The notebook walks through data preparation, modeling, evaluation, and insights, offering a clear and reproducible workflow for experimenting with recommender systems.
A Streamlit application demonstrating Reinforcement Learning (RL) for intelligent product recommendations in online advertising. Explore different RL algorithms and their impact on personalization.
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.
AI-powered E-Commerce Product Recommender System built with Python and Streamlit, developed during my Micro IT internship to provide personalized product suggestions based on user selections.
Real-time product recommendation system built using Apache Spark, Kafka, and Python.
Robust product recommendations using topological data analysis. 4-week project completed during Insight Fellows Program, AI Silicon Valley 2020 B Cohort
No description provided.
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.
No description provided.
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.
BuildML Project