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The project is based on a Hybrid recommendation engine that uses both Collaborative as well as Content based filtering methods to suggest streamers to the online users based on the type content they consume.
The goal of this project is to implement a Hybrid Recommender System that combines item-based and user-based recommendation methods to provide movie recommendations for a specific user. The system aims to offer a total of 10 movie recommendations by using both methods.
The objective of the competition was to create the best recommender system for a book recommendation service by providing 10 recommended books to each user. The evaluation metric was MAP@10.
🎓 Final Project for Completing Bachelor Degree in Petra Christian University. Create Hybrid Recommender System for Interior Products and its Services using Data Implicit Feedback
This repository houses the codebase for a Book Recommendation System, crafted using collaborative filtering, Flask, and cosine similarity. The system employs advanced machine learning techniques to generate personalized book recommendations based on user preferences.
Combines user-based and item-based recommendation systems to deliver personalized movie suggestions, utilizing user preferences and film characteristics.
Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.
Public repository for the Isle of Wight Supply Chain (IWSC) dataset and the Transitive Semantic Relationships (TSR) inference algorithm for cold-start recommendations.
This repo contains my practice and template code for all kinds of recommender systems using SupriseLib. More complex and hybrid Recommender Systems can build on top of these template codes.