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
Code for ICML2019 Paper "Compositional Invariance Constraints for Graph Embeddings"

Dependencies NOTE: This code has been updated, if you were using this repo earlier and experienced issues that was due to an outaded codebase. Please

Avishek (Joey) Bose 43 Nov 25, 2022
Pytorch domain library for recommendation systems

TorchRec (Experimental Release) TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale

Meta Research 1.3k Jan 05, 2023
A recommendation system for suggesting new books given similar books.

Book Recommendation System A recommendation system for suggesting new books given similar books. Datasets Dataset Kaggle Dataset Notebooks goodreads-E

Sam Partee 2 Jan 06, 2022
fastFM: A Library for Factorization Machines

Citing fastFM The library fastFM is an academic project. The time and resources spent developing fastFM are therefore justified by the number of citat

1k Dec 24, 2022
The source code for "Global Context Enhanced Graph Neural Network for Session-based Recommendation".

GCE-GNN Code This is the source code for SIGIR 2020 Paper: Global Context Enhanced Graph Neural Networks for Session-based Recommendation. Requirement

98 Dec 28, 2022
A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information" (WSDM 2021)

FairGNN A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information" (

31 Jan 04, 2023
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

DANSER-WWW-19 This repository holds the codes for Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recom

Qitian Wu 78 Dec 10, 2022
Elliot is a comprehensive recommendation framework that analyzes the recommendation problem from the researcher's perspective.

Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

Information Systems Lab @ Polytechnic University of Bari 215 Nov 29, 2022
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

57 Nov 03, 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 library of metrics for evaluating recommender systems

recmetrics A python library of evalulation metrics and diagnostic tools for recommender systems. **This library is activly maintained. My goal is to c

Claire Longo 458 Jan 06, 2023
Recommender systems are the systems that are designed to recommend things to the user based on many different factors

Recommender systems are the systems that are designed to recommend things to the user based on many different factors. The recommender system deals with a large volume of information present by filte

Happy N. Monday 3 Feb 15, 2022
NVIDIA Merlin is an open source library designed to accelerate recommender systems on NVIDIA’s GPUs.

NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in

420 Jan 04, 2023
A framework for large scale recommendation algorithms.

A framework for large scale recommendation algorithms.

Alibaba Group - PAI 880 Jan 03, 2023
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
Knowledge-aware Coupled Graph Neural Network for Social Recommendation

KCGN AAAI-2021 《Knowledge-aware Coupled Graph Neural Network for Social Recommendation》 Environments python 3.8 pytorch-1.6 DGL 0.5.3 (https://github.

xhc 22 Nov 18, 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
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
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
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