recommendation-system | Movie recommendation system using statistics and machine | Analytics library

by vivamoto R Version: Current License: No License

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kandi X-RAY | recommendation-system Summary

recommendation-system is a R library typically used in Analytics applications. recommendation-system has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

This repository contains the report and code of the capstone project of HarvardX’s Data Science Professional Certificate program. The goal is to build and evaluate a movie recommendation system applying the lessons learned in the program. The HTML version is available on RPubs. code.R - R code used to build and evaluate the machine learning models. report.Rmd - R Markdown code used to create the PDF and HTML reports. report.pdf - Technical report with the model building and evaluation.

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recommendation-system has a low active ecosystem.

It has 2 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

recommendation-system has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of recommendation-system is current.

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recommendation-system has 0 bugs and 0 code smells.

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recommendation-system has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

recommendation-system code analysis shows 0 unresolved vulnerabilities.

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recommendation-system Key Features

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Community Discussions

Trending Discussions on recommendation-system

IndexError when accessing a certain column with pandas

Is there a way to import a csv into Neo4j using foreach or unwind?

semantic content recommendation system with Amazon SageMaker, storing in S3

Bypassing 403 while scraping

Creating MLP model to predict the ratings that a user will give to an unseen movie using PyTorch

QUESTION

IndexError when accessing a certain column with pandas

Asked 2022-Feb-21 at 16:42

When trying to access the label column, I get:

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ANSWER

Answered 2022-Feb-21 at 16:42

Looks like the problem turned out to be, like @hpaulj said, that pd.DataFrame(self.data).head() line, because not only it was called for nothing, since it wasn't assigned to any variable, but also returned the first 5 rows, which makes sense now why I got a KeyError regarding the index after making a first change. So, instead, I changed it to self.data = pd.DataFrame(self.data) and now the code works as expected to.

Source https://stackoverflow.com/questions/71202844

QUESTION

Is there a way to import a csv into Neo4j using foreach or unwind?

Asked 2021-Dec-01 at 21:25

I am using the following .csv file for Neo4j import. There are 202 rackets. The numbers below racketX are the rating the user has given that racket.

I want to create the relationships among the users and the rating they have given to each racket. This is my current approach:

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ANSWER

Answered 2021-Dec-01 at 21:25

I would break down the load into multiple steps.

Load the users.

Source https://stackoverflow.com/questions/70186540

QUESTION

semantic content recommendation system with Amazon SageMaker, storing in S3

Asked 2021-Jun-07 at 04:41

I am fairly new to AWS and Sagemaker and have decided to follow some of the tutorials Amazon has to familiarize myself with it. I've been following this one (tutorial) and I've realized that it's an older tutorial using Sagemaker v1. I've been able to look up and change whatever is needed for the tutorial to work in v2 but I became stuck at this part for storing the training data in a S3 bucket to deploy the model.

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ANSWER

Answered 2021-Jun-07 at 02:39

It looks like they've left some of the code out, or changed the terminology and left in predictions by accident. predictions is an object that is defined on this page https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-test-model.html

You'll have to work out what predictions is in your case.

Source https://stackoverflow.com/questions/67863816

QUESTION

Bypassing 403 while scraping

Asked 2020-Nov-01 at 15:42

I want to reproduce the results of this article on how to make your own recommendation system. Basically she starts scraping the page https://www.nosetime.com/pinpai/2-a.html in this notebook to get the names of the perfumes. I tried to do the same but I get an error 403 with requests.get(url). Then I tried to use the same solution as in this answer, a proxy, but got the same error:

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ANSWER

Answered 2020-Nov-01 at 15:42

Set User-Agent HTTP header to obtain correct response from the server:

Source https://stackoverflow.com/questions/64633573

QUESTION

Creating MLP model to predict the ratings that a user will give to an unseen movie using PyTorch

Asked 2020-Jul-25 at 22:40

For my project , i’m trying to predict the ratings that a user will give to an unseen movie, based on the ratings he gave to other movies. I’m using the movielens dataset.The Main folder, which is ml-100k contains informations about 100,000 movies.

Before processing the data, the main data (ratings data) contains user ID, movie ID, user rating from 0 to 5 and timestamps(not considered for this project).I then split the data into Training set(80%) and test data(20%) using sklearn Library.

To create the recommendation systems, the model ‘Stacked-Autoencoder’ is being used. I’m using PyTorch and the code is implemented on Google Colab. The project is based on this https://towardsdatascience.com/stacked-auto-encoder-as-a-recommendation-system-for-movie-rating-prediction-33842386338

I'm new to deep Learning and I want to compare this model(Stacked_Autoencoder) to another Deep Learning model. For Instance,I want to use Multilayer Perception(MLP). This is for research purposes. This is the code below for creating Stacked-Autoencoder model and training the model.

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ANSWER

Answered 2020-Jul-25 at 22:40

An MLP is not suited for recommendations. If you want to go this route, you will need to create an embedding for your userid and another for your itemid and then add linear layers on top of the embeddings. Your target will be to predict the rating for a userid-itemid pair.

I suggest you take a look at variational autoencoders (VAE). They give state-of-the-art results in recommender systems. They will also give a fair comparaison with your stacked-autoencoder. Here's the research paper applying VAE for collaborative filtering : https://arxiv.org/pdf/1802.05814.pdf

Source https://stackoverflow.com/questions/62839095

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