Persine is an automated tool to study and reverse-engineer algorithmic recommendation systems.

Overview

Documentation Status

Persine, the Persona Engine

Persine is an automated tool to study and reverse-engineer algorithmic recommendation systems. It has a simple interface and encourages reproducible results. You tell Persine to drive around YouTube and it gives back a spreadsheet of what else YouTube suggests you watch!

Persine => Pers[ona Eng]ine

For example!

People have suggested that if you watch a few lightly political videos, YouTube starts suggesting more and more extreme content – but does it really?

The theory is difficult to test since it involves a lot of boring clicking and YouTube already knows what you usually watch. Persine to the rescue!

  1. Persine starts a new fresh-as-snow Chrome
  2. You provide a list of videos to watch and buttons to click (like, dislike, "next up" etc)
  3. As it watches and clicks more and more, YouTube customizes and customizes
  4. When you're all done, Persine will save your winding path and the video/playlist/channel recommendations to nice neat CSV files.

Beyond analysis, these files can be used to repeat the experiment again later, seeing if recommendations change by time, location, user history, etc.

If you didn't quite get enough data, don't worry – you can resume your exploration later, picking up right where you left off. Since each "persona" is based on Chrome profiles, all your cookies and history will be safely stored until your next run.

An actual example

See Persine in action on Google Colab.

Includes a few examples for analysis, too.

Installation

pip install persine

Persine will automatically install Selenium and BeautifulSoup for browsing/scraping, pandas for data analysis, and pillow for processing screenshots.

You will need to manually install chromedriver to allow Selenium to control Chrome. See details here

Quickstart

In this example, we start a new session by visiting a YouTube video and clicking the "next up" video three times to see where it leads us. We then save the results for later analysis.

from persine import PersonaEngine

engine = PersonaEngine(headless=False)

with engine.persona() as persona:
    persona.run("https://www.youtube.com/watch?v=hZw23sWlyG0")
    persona.run("youtube:next_up#3")
    persona.history.to_csv("history.csv")
    persona.recommendations.to_csv("recs.csv")

We turn off headless mode because it's fun to watch!

More examples, more features, more everything

Find the complete documentation here

Owner
Jonathan Soma
baby data journo wrangler @ledeprogram + @littlecolumns, cat wrangler @cat-republic
Jonathan Soma
Mutual Fund Recommender System. Tailor for fund transactions.

Explainable Mutual Fund Recommendation Data Please see 'DATA_DESCRIPTION.md' for mode detail. Recommender System Methods Baseline Collabarative Fiilte

JHJu 2 May 19, 2022
A Python scikit for building and analyzing recommender systems

Overview Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data. Surprise was designed with th

Nicolas Hug 5.7k Jan 01, 2023
Learning Fair Representations for Recommendation: A Graph-based Perspective, WWW2021

FairGo WWW2021 Learning Fair Representations for Recommendation: A Graph-based Perspective As a key application of artificial intelligence, recommende

lei 39 Oct 26, 2022
A Python implementation of LightFM, a hybrid recommendation algorithm.

LightFM Build status Linux OSX (OpenMP disabled) Windows (OpenMP disabled) LightFM is a Python implementation of a number of popular recommendation al

Lyst 4.2k Jan 02, 2023
Implementation of a hadoop based movie recommendation system

Implementation-of-a-hadoop-based-movie-recommendation-system 通过编写代码,设计一个基于Hadoop的电影推荐系统,通过此推荐系统的编写,掌握在Hadoop平台上的文件操作,数据处理的技能。windows 10 hadoop 2.8.3 p

汝聪(Ricardo) 5 Oct 02, 2022
Movie Recommender System

Movie-Recommender-System Movie-Recommender-System is a web application using which a user can select his/her watched movie from list and system will r

1 Jul 14, 2022
This is our implementation of GHCF: Graph Heterogeneous Collaborative Filtering (AAAI 2021)

GHCF This is our implementation of the paper: Chong Chen, Weizhi Ma, Min Zhang, Zhaowei Wang, Xiuqiang He, Chenyang Wang, Yiqun Liu and Shaoping Ma. 2

Chong Chen 53 Dec 05, 2022
The official implementation of "DGCN: Diversified Recommendation with Graph Convolutional Networks" (WWW '21)

DGCN This is the official implementation of our WWW'21 paper: Yu Zheng, Chen Gao, Liang Chen, Depeng Jin, Yong Li, DGCN: Diversified Recommendation wi

FIB LAB, Tsinghua University 37 Dec 18, 2022
Code for MB-GMN, SIGIR 2021

MB-GMN Code for MB-GMN, SIGIR 2021 For Beibei data, run python .\labcode.py For Tmall data, run python .\labcode.py --data tmall --rank 2 For IJCAI

32 Dec 04, 2022
Fast Python Collaborative Filtering for Implicit Feedback Datasets

Implicit Fast Python Collaborative Filtering for Implicit Datasets. This project provides fast Python implementations of several different popular rec

Ben Frederickson 3k Dec 31, 2022
Deep recommender models using PyTorch.

Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various poin

Maciej Kula 2.8k Dec 29, 2022
It is a movie recommender web application which is developed using the Python.

Movie Recommendation 🍿 System Watch Tutorial for this project Source IMDB Movie 5000 Dataset Inspired from this original repository. Features Simple

Kushal Bhavsar 10 Dec 26, 2022
Hierarchical Fashion Graph Network for Personalized Outfit Recommendation, SIGIR 2020

hierarchical_fashion_graph_network This is our Tensorflow implementation for the paper: Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, and

LI Xingchen 70 Dec 05, 2022
Bert4rec for news Recommendation

News-Recommendation-system-using-Bert4Rec-model Bert4rec for news Recommendation

saran pandian 2 Feb 04, 2022
Incorporating User Micro-behaviors and Item Knowledge 59 60 3 into Multi-task Learning for Session-based Recommendation

MKM-SR Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation Paper data and code This is the

ciecus 38 Dec 05, 2022
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer

Introduction This is the repository of our accepted CIKM 2021 paper "Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Trans

SeqRec 29 Dec 09, 2022
EXEMPLO DE SISTEMA ESPECIALISTA PARA RECOMENDAR SERIADOS EM PYTHON

exemplo-de-sistema-especialista EXEMPLO DE SISTEMA ESPECIALISTA PARA RECOMENDAR SERIADOS EM PYTHON Resumo O objetivo de auxiliar o usuário na escolha

Josue Lopes 3 Aug 31, 2021
Recommendation System to recommend top books from the dataset

recommendersystem Recommendation System to recommend top books from the dataset Introduction The recom.py is the main program code. The dataset is als

Vishal karur 1 Nov 15, 2021
Recommender System Papers

Included Conferences: SIGIR 2020, SIGKDD 2020, RecSys 2020, CIKM 2020, AAAI 2021, WSDM 2021, WWW 2021

RUCAIBox 704 Jan 06, 2023
Plex-recommender - Get movie recommendations based on your current PleX library

plex-recommender Description: Get movie/tv recommendations based on your current

5 Jul 19, 2022