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>
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
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
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
A framework for large scale recommendation algorithms.

A framework for large scale recommendation algorithms.

Alibaba Group - PAI 880 Jan 03, 2023
Bert4rec for news Recommendation

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

saran pandian 2 Feb 04, 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
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
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
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
Accuracy-Diversity Trade-off in Recommender Systems via Graph Convolutions

Accuracy-Diversity Trade-off in Recommender Systems via Graph Convolutions This repository contains the code of the paper "Accuracy-Diversity Trade-of

2 Sep 16, 2022
Detecting Beneficial Feature Interactions for Recommender Systems, AAAI 2021

Detecting Beneficial Feature Interactions for Recommender Systems (L0-SIGN) This is our implementation for the paper: Su, Y., Zhang, R., Erfani, S., &

26 Nov 22, 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
基于个性化推荐的音乐播放系统

MusicPlayer 基于个性化推荐的音乐播放系统 Hi, 这是我在大四的时候做的毕设,现如今将该项目开源。 本项目是基于Python的tkinter和pygame所著。 该项目总体来说,代码比较烂(因为当时水平很菜)。 运行的话安装几个基本库就能跑,只不过里面的数据还没有上传至Github。 先

Cedric Niu 6 Nov 19, 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
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
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
6002project-rl - An implemention of offline RL on recommender system

An implemention of offline RL on recommender system @author: misajie @update: 20

Tzay Lee 3 May 24, 2022
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
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