Recommendations with IBM Data (knowledge-based plus collaborative filtering both model-based and neighborhood-based)

A recommendation system for IBM Watson Studio platform that analyzes user-article interactions and suggests relevant content using multiple recommendation techniques

Content recommendation API

Analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles.

A platform to read and share 📚 with other users, the platform tracks users by collecting there data and exposing the collected data as an API

Recommendation engine for IBM Watson.

An audio book recommendation engine for Audible users.

EDA, Pre-processing, 6 Recommendation Systems Techniques: * Popularity-Based, * Cosine Similarity Collaborative Filtering, * Matrix Factorization Collaborative Filtering, * Clustering, * Content-Based Filtering, * Hybrid Recommendation System.

Auto Complete / Suggestion feature using Trie data structure

A recommendationn system for movies using Python and machine learning algorithms (k nearest neighbours, logistic regression). numpy. scikit-learn

Using XGboost to predict accommodation listing prices

A modern, responsive web application that delivers personalized content recommendations based on user preferences and behavior. This interactive recommendation system allows users to discover content tailored to their interests through category selection, tag filtering, and customizable content parameters.

This project is to analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles they might like. Recommending articles that are most pertinent to specific users is beneficial to both service providers and users.

Articles recommendation engine for IBM Watson Studio platform

This repository analyze user interactions with articles on the IBM Watson Studio platform and develop recommendation systems to suggest new articles that align with their interests.

P&G Hack - Recommendation platform

Sixth project of Udacity data scientist nanodegree

A Django-powered book recommendation engine with Bootstrap 5 frontend. Features personalized suggestions, user profiles, and rating system. Clean, responsive UI with landing page, authentication flows, and accessible design. Lightweight implementation using plain CSS (no SCSS) for easy customization.