Plex-recommender - Get movie recommendations based on your current PleX library

Overview

plex-recommender

Description: Get movie/tv recommendations based on your current PleX library. It will go through each item in the list, pull the number of recommendations you request, tally them, and then print out an ordered list of the most recommended items based on your inputs.

Notes: In order for it to work you will need a TMDB API key, which is free.

The script uses no external libraries, and is built for Python 3. There is little in the way for error checking bad user input.

Creating .json: I recommend using Tautulli to create the main .json for ingestion, the script is built around this specific export format. To do that, go to the library you want to export, choose 'Export Metadata' and set the following different from default:

  • Data File Format: JSON
  • Custom Metadata Fields: guids.id
  • Media Info Export Level: Level 0 - None / Custom

From there, all you have to do is run the script and select the previously created .json.

Example usage and output:

python .\recommender.py

(R)ecommended - From "recommended" list on media page.
(S)imilar - From similar keywords and genres.
Select recommendation type (R/S):
        s
Enter media type (Movie/TV):
        m
Enter number of recommendations (range 1-20):
        10
Enter TMDB API key:
        xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
Enter full path to Tautulli metadata output .json with guids.id field:
        ".\Library - Movies - All [1].json"


Extracting TMDB IDs from list of media at:
        .\Library - Movies - All [1].json...

Getting 10 TMDB recommendations for each of the 1166 IDs...
        Progress: |██████████████████████████████| 100.00% Complete

1 titles with no recommendations:
        Zombie Cop (1991)

Total recommendations:
        7947

Top 10 recommendations:
        Movie - Death Race (2008)
        TMDB - https://www.themoviedb.org/movie/10483
        IMDB - https://www.imdb.com/title/tt0452608/
        Recommended - 125 times

<snip>
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
Books Recommendation With Python

Books-Recommendation Business Problem During the last few decades, with the rise

Çağrı Karadeniz 7 Mar 12, 2022
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

Annoy Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given quer

Spotify 10.6k Jan 01, 2023
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation

RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation Pytorch based implemention of Relational Temporal

28 Dec 28, 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
Codes for AAAI'21 paper 'Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation'

DHCN Codes for AAAI 2021 paper 'Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation'. Please note that the default link

Xin Xia 124 Dec 14, 2022
An open source movie recommendation WebApp build by movie buffs and mathematicians that uses cosine similarity on the backend.

Movie Pundit Find your next flick by asking the (almost) all-knowing Movie Pundit Jump to Project Source » View Demo · Report Bug · Request Feature Ta

Kapil Pramod Deshmukh 8 May 28, 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
Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

MGNN-SPred This is our Tensorflow implementation for the paper: WenWang,Wei Zhang, Shukai Liu, Qi Liu, Bo Zhang, Leyu Lin, and Hongyuan Zha. 2020. Bey

Wen Wang 18 Jan 02, 2023
Use Jupyter Notebooks to demonstrate how to build a Recommender with Apache Spark & Elasticsearch

Recommendation engines are one of the most well known, widely used and highest value use cases for applying machine learning. Despite this, while there are many resources available for the basics of

International Business Machines 793 Dec 18, 2022
Recommendation Systems for IBM Watson Studio platform

Recommendation-Systems-for-IBM-Watson-Studio-platform Project Overview In this project, I analyze the interactions that users have with articles on th

Milad Sadat-Mohammadi 1 Jan 21, 2022
Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks

SR-HGNN ICDM-2020 《Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks》 Environments python 3.8 pytorch-1.6 DGL 0.5.

xhc 9 Nov 12, 2022
The implementation of the submitted paper "Deep Multi-Behaviors Graph Network for Voucher Redemption Rate Prediction" in SIGKDD 2021 Applied Data Science Track.

DMBGN: Deep Multi-Behaviors Graph Networks for Voucher Redemption Rate Prediction The implementation of the accepted paper "Deep Multi-Behaviors Graph

10 Jul 12, 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
Cross-Domain Recommendation via Preference Propagation GraphNet.

PPGN Codes for CIKM 2019 paper Cross-Domain Recommendation via Preference Propagation GraphNet. Citation Please cite our paper if you find this code u

Information Retrieval Group, Wuhan University, China 20 Dec 15, 2022
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
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
A framework for large scale recommendation algorithms.

A framework for large scale recommendation algorithms.

Alibaba Group - PAI 880 Jan 03, 2023
A TensorFlow recommendation algorithm and framework in Python.

TensorRec A TensorFlow recommendation algorithm and framework in Python. NOTE: TensorRec is not under active development TensorRec will not be receivi

James Kirk 1.2k Jan 04, 2023
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