This repo contains all files needed for building a recommender system based on 2019 Yelp Challenge Datasets. This is the No.1 solution in USC Viterbi Data Mining Competition.
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Updated
Jan 2, 2023 - Python
This repo contains all files needed for building a recommender system based on 2019 Yelp Challenge Datasets. This is the No.1 solution in USC Viterbi Data Mining Competition.
USC DSCI 553 - Foundations & Applications of Data Mining - Spring 2024 - Prof. Wei-Min Shen
The project's goal is to create diverse recommendation systems that predict user-item ratings
The assignment comprises two main tasks: implementing LSH to identify similar businesses based on user ratings and developing various collaborative filtering recommendation systems to predict user ratings for businesses.
This project developed and optimized a hybrid recommendation system that processes over 450,000 training data points and 142,000 validation data points. The system combines user ratings, merchant details, and user reviews to predict users' ratings for restaurants they have not visited.
Recommendation algorithms
An application that uses the algorithm of user-based collaborative filtering and item-based collaborative filtering to recommend new movies
Association Rule Learning, Content Based Recommendation, Item Based Collaborative, Filtering User Based Collaborative Filtering, Model Based Matrix Factorization projects i've done about
In this repository, I implement a recommender system using matrix factorization. Here, two types of RS are implemented. First, use the factorized matrix for user and item. and second, rebuild the Adjacency matrix. both approaches are acceptable and implemented in this repo. To factorized the matrix, funk-svd Algorithm is used. you can find his i…
A web application to recommend music to users based on machine learning algorithms such as item-based & user-based collaborative filtering and kNN.
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
Combines user-based and item-based recommendation systems to deliver personalized movie suggestions, utilizing user preferences and film characteristics.
TensorFlow2 Implementation of "Neural Attentive Item Similarity Model for Recommendation"
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