You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This project is focused on building a movie recommendation system using the MovieLens dataset. The system leverages several machine learning techniques to provide personalized movie recommendations based on user preferences and past behaviors.
Book Recommendation model based on LightFM library. Unlike models focusing only on Collaborative Filtering method, LightFM allows the model to face the cold-start problem by implementing user's features in case of missing ratings. Dataset: https://www.kaggle.com/datasets/arashnic/book-recommendation-dataset/data
It is a Context-Aware Implicit Feedback based Hotel Recommender System for Anonymous Business Travellers. This project is part of my master thesis project.
Recommendation engine with a .97 AUC achieved using clustering techniques to create user features. Data represents Olist marketplace transactions and was retrieved from kaggle.com.
Comparison of two approaches for building a recommender system presented. The first one is a collaborative filtering. The second one is hybrid recommender system. This project is the second stage of a contest for an internship in VK.