pairwise-distances

There are 0 repository under pairwise-distances topic.

JuliaStats / Distances.jl
A Julia package for evaluating distances (metrics) between vectors.
julia distance pairwise-distances distance-evaluation
Language:Julia 412
oliviaguest / pdist
Calculate mean of pairwise weighted distances between points using great circle metric.
geographical-information-system pairwise-distances pairwise-weighted-distances python cython big-data
Language:Python 11
seth-brown / furlong
A zero-dependency Typescript library for computing pairwise distances
euclidean vector-distance pairwise-distances
Language:TypeScript 7
RozaAbolghasemi / Group_Recommendation_Syatem_GcPp_clustering
A Jupyter notebook for a project centered around 'Group Recommendation Systems (GRS)' utilizing the 'GcPp' clustering approach.
recommendation-system machine-learning clustering data-science graph pytorch singnn deep-learning neural-networks group-recommendations group-recommendation-system decision-making graph-edit-distance group-decision-making graph-convolutional-network recommender-system group-recommender pairwise-comparison pairwise-distances pairwise-preferences
Language:Jupyter Notebook 5
Pradnya1208 / Similar-product-recommendation-system-using-CNN
In this repository, we have implemented the CNN based recommendation system for finding similar products.
recommender-system similar-product-recommender fashion-dataset cnn-model fashion-embedding embeddings resnet-50 imagenet cosine-similarity cosine-distance pairwise-distances
Language:Jupyter Notebook 4
sanketmaneDS / Recommendation_Engine
This repository contains introductory notebooks for recommendation system.
content-based-recommendation cosine-similarity pairwise-distances user-based-recommendation
Language:Jupyter Notebook 4
abhinavnatarajan / RedClust.jl
Julia package to perform Bayesian clustering of high-dimensional Euclidean data using pairwise dissimilarity information.
bayesian-statistics clustering-algorithm high-dimensional-statistics pairwise-distances
Language:Julia 3
Abhik35 / Assignment-Recommendation-System-Data-Mining-books-
Recommend a best book based on the ratings: Sort by User IDs number of unique users in the dataset number of unique books in the dataset converting long data into wide data using pivot table Replacing the index values by unique user Ids Impute those NaNs with 0 values Calculating Cosine Similarity between Users on array data Store the results in a dataframe format Set the index and column names to user ids Nullifying diagonal values Most Similar Users extract the books which userId 162107 & 276726 have watched extract the books which userId 276729 & 276726 have watched
python data-mining correlation numpy sklearn pandas recommender-system scipy cosine-similarity pairwise-distances
Language:Jupyter Notebook 1
kunal-mallick / Book_Recommendation
We are proud to introduce our new book recommendation system, book.io. This system uses the user-to-user collaborative filtering model to recommend books to users based on their preferences and ratings.
book-recomendation collaborative-filtering machine-learning matplotlib numpy pairwise-distances pandas pickle python re seaborn sklearn streamlit user-to-user
Language:Jupyter Notebook 1
MANOJKUMAR-449 / RECOMENDATION-ENGINE
numpy pandas matplotlib python unsupervised-learning recommendation-system cosine-distance correlation pivot-tables pairwise-distances similarity-matrix
Language:Jupyter Notebook 1
shanuhalli / Assignment-Recommendation-System
Build a recommender system by using cosine simillarties score - books dataset.
assignment-10 collaborative-filtering correlation cosine-similarity data-science machine-learning pairwise-distances recommendation-system recommender-system
Language:Jupyter Notebook 1
vaitybharati / Assignment-10-Recommendation-System-Data-Mining-books-
Assignment-10-Recommendation-System-Data-Mining-books. Recommend a best book based on the ratings: Sort by User IDs, number of unique users in the dataset, number of unique books in the dataset, converting long data into wide data using pivot table, replacing the index values by unique user Ids, Impute those NaNs with 0 values, Calculating Cosine Similarity between Users on array data, Store the results in a dataframe format, Set the index and column names to user ids, Nullifying diagonal values, Most Similar Users, extract the books which userId 162107 & 276726 have watched, extract the books which userId 276729 & 276726 have watched.
correlation cosine-similarity data-mining numpy pairwise-distances pandas python recommendation-system scipy sklearn
Language:Jupyter Notebook 1
vaitybharati / P35.-Unsupervised-ML---Recommendation-System-Data-Mining-Movies-
Unsupervised-ML-Recommendation-System-Data-Mining-Movies. Recommend movies based on the ratings: Sort by User IDs, number of unique users in the dataset, number of unique movies in the dataset, Impute those NaNs with 0 values, Calculating Cosine Similarity between Users on array data, Store the results in a dataframe format, Set the index and column names to user ids, Slicing first 5 rows and first 5 columns, Nullifying diagonal values, Most Similar Users, extract the movies which userId 6 & 168 have watched.
correlation cosine cosine-similarity data-mining imputation metrics numpy pairwise-distances pandas python recommendation-system scipy sklearn unsupervised-learning
Language:Jupyter Notebook 1
vaitybharati / Recommendation-Engine
Recommendation-Engine
python3 recommendation-engine cosine-similarity pairwise-distances sklearn-metrics
Language:Jupyter Notebook 1
paul-lindquist / spotify-recommendation-system
Built a content-based recommendation/recommender system specific to electronic music on Spotify using K-Nearest Neighbors (KNN), cosine similarity and sigmoid function kernel to generate similarity and distance-based recommendations. Video of the project presentation: https://lnkd.in/gq5w-4Wm
python k-nearest-neighbors cosine-similarity pairwise-comparisons pairwise-distances modeling python-3 python3 jupyter-notebook
Language:Jupyter Notebook 0
rajeevvhanhuve / Book-Rental-Recommendation
Machine Learning
mahcine-learning pairwise-distances recommendation-system sklearn
Language:Jupyter Notebook 0
saikrishnabudi / Recommendation-System
Data Science - Recommendation Work
collaborative-filtering correlation cosine-similarity machine-learning pairwise-distances recommendation-system
Language:Jupyter Notebook

pairwise-distances

JuliaStats / Distances.jl

oliviaguest / pdist

seth-brown / furlong

RozaAbolghasemi / Group_Recommendation_Syatem_GcPp_clustering

Pradnya1208 / Similar-product-recommendation-system-using-CNN

sanketmaneDS / Recommendation_Engine

abhinavnatarajan / RedClust.jl

Abhik35 / Assignment-Recommendation-System-Data-Mining-books-

kunal-mallick / Book_Recommendation

MANOJKUMAR-449 / RECOMENDATION-ENGINE

shanuhalli / Assignment-Recommendation-System

vaitybharati / Assignment-10-Recommendation-System-Data-Mining-books-

vaitybharati / P35.-Unsupervised-ML---Recommendation-System-Data-Mining-Movies-

vaitybharati / Recommendation-Engine

paul-lindquist / spotify-recommendation-system

rajeevvhanhuve / Book-Rental-Recommendation

saikrishnabudi / Recommendation-System