BoardGameGeek Recommender System is a start-to-finish project, from sourcing the data to a hybrid recommender system utilizing both content-based and collaborative filtering.

A Comprehensive Framework for Building End-to-End Recommendation Systems with State-of-the-Art Models

War : Hi !

ML project on movie recommendation systems, showcasing collaborative filtering and content-based approaches using Python relevant libraries.

Gorse open source recommender system engine

Trend Fitness is a web application dedicated to providing professional fitness advice which will include a range from fitness plans to diet plans catered to every individual needs. I believe that my web application will embark on a transformative journey towards a healthier lifestyle.

Data Science Internship

Learn Hub is a web application that helps to learn any technology in a structured & organized way. The idea is to enhance Internet learning by producing search query results based on higher accuracy and relevance of the content, instead of traditional ranking methods.

A Comparative Framework for Multimodal Recommender Systems

building a movie recommendation system using collaborative filtering techniques.

The topic is about product matching via Machine Learning. This involves using various machine learning techniques such as natural language processing, image recognition, and collaborative filtering algorithms to match similar products together.

Movie Recommender (School Project)

[ICDE'2024] "GraphAug: Graph Augmentation for Recommendation"

A highly-modularized and recommendation-efficient recommendation library based on PyTorch.

Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.

웹 기사 추천 AI 경진대회, DACON (2024.06.03 ~ 2024.07.01)

Collaborative and hybrid recommendation systems

Optimize sales while better understand the customers through Classification, Recommendation, and Deep Learning analysis.

A movie recommend system based on collaborative filtering algorithm.

This project utilizes PySpark DataFrames and PySpark RDD to implement item-based collaborative filtering. By calculating cosine similarity scores or identifying movies with the highest number of shared viewers, the system recommends 10 similar movies for a given target movie that aligns users’ preferences.