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
A Library for Field-aware Factorization Machines

Table of Contents ================= - What is LIBFFM - Overfitting and Early Stopping - Installation - Data Format - Command Line Usage - Examples -

1.6k Dec 05, 2022
Self-supervised Graph Learning for Recommendation

SGL This is our Tensorflow implementation for our SIGIR 2021 paper: Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian,and Xing

151 Dec 20, 2022
Collaborative variational bandwidth auto-encoder (VBAE) for recommender systems.

Collaborative Variational Bandwidth Auto-encoder The codes are associated with the following paper: Collaborative Variational Bandwidth Auto-encoder f

Yaochen Zhu 14 Dec 11, 2022
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems

RecSim NG, a probabilistic platform for multi-agent recommender systems simulation. RecSimNG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a power

Google Research 110 Dec 16, 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
Cloud-based recommendation system

This project is based on cloud services to create data lake, ETL process, train and deploy learning model to implement a recommendation system.

Yi Ding 1 Feb 02, 2022
E-Commerce recommender demo with real-time data and a graph database

🔍 E-Commerce recommender demo 🔍 This is a simple stream setup that uses Memgraph to ingest real-time data from a simulated online store. Data is str

g-despot 3 Feb 23, 2022
Attentive Social Recommendation: Towards User And Item Diversities

ASR This is a Tensorflow implementation of the paper: Attentive Social Recommendation: Towards User And Item Diversities Preprint, https://arxiv.org/a

Dongsheng Luo 1 Nov 14, 2021
Movies/TV Recommender

recommender Movies/TV Recommender. Recommends Movies, TV Shows, Actors, Directors, Writers. Setup Create file API_KEY and paste your TMDB API key in i

Aviem Zur 3 Apr 22, 2022
Spark-movie-lens - An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset

A scalable on-line movie recommender using Spark and Flask This Apache Spark tutorial will guide you step-by-step into how to use the MovieLens datase

Jose A Dianes 794 Dec 23, 2022
A movie recommender which recommends the movies belonging to the genre that user has liked the most.

Content-Based-Movie-Recommender-System This model relies on the similarity of the items being recommended. (I have used Pandas and Numpy. However othe

Srinivasan K 0 Mar 31, 2022
An Efficient and Effective Framework for Session-based Social Recommendation

SEFrame This repository contains the code for the paper "An Efficient and Effective Framework for Session-based Social Recommendation". Requirements P

Tianwen CHEN 23 Oct 26, 2022
This library intends to be a reference for recommendation engines in Python

Crab - A Python Library for Recommendation Engines

Marcel Caraciolo 85 Oct 04, 2021
Group-Buying Recommendation for Social E-Commerce

Group-Buying Recommendation for Social E-Commerce This is the official implementation of the paper Group-Buying Recommendation for Social E-Commerce (

Jun Zhang 37 Nov 28, 2022
reXmeX is recommender system evaluation metric library.

A general purpose recommender metrics library for fair evaluation.

AstraZeneca 258 Dec 22, 2022
QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

QRec is a Python framework for recommender systems (Supported by Python 3.7.4 and Tensorflow 1.14+) in which a number of influential and newly state-of-the-art recommendation models are implemented.

Yu 1.4k Dec 27, 2022
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
Recommender System Papers

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

RUCAIBox 704 Jan 06, 2023
A library of Recommender Systems

A library of Recommender Systems This repository provides a summary of our research on Recommender Systems. It includes our code base on different rec

MilaGraph 980 Jan 05, 2023
Graph Neural Network based Social Recommendation Model. SIGIR2019.

Basic Information: This code is released for the papers: Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang and Meng Wang. A Neural Influence Dif

PeijieSun 144 Dec 29, 2022