NVIDIA/DeepRecommender

Add MovieLens example

Open

#2 opened on Oct 3, 2017

View on GitHub
 (0 comments) (2 reactions) (0 assignees)Python (340 forks)auto 404
enhancementgood first issuehelp wanted

Repository metrics

Stars
 (1,702 stars)
PR merge metrics
 (PR metrics pending)

Description

Provide a recepie for training a model on MovieLens data (20M and 1M).

This should include the following:

  1. Data converter. Consider fixing/adjusting this script
  • Train/Eval/Test split. Training test ratings should come before any Eval and Test ratings. See section 3.1 (Experiment setup) of this paper for details and Netflix converter for example.

  1. An example train.sh, and test.sh for 20M and 1M MovieLens data sets which will train/eval and test the model correspondingly.

  2. Choose model architecture and hyper parameters that give best eval RMSE you can get.

Contributor guide

Research direction
Explore the NVIDIA DeepRecommender codebase to understand the existing model architecture and data pipeline. Then implement a data converter for MovieLens dataset following the provided reference scripts, create train/test scripts, and tune hyperparameters for best RMSE.
Tech stack
pythonshell
Domain
machine learningdatabackend
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
3-5 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonMachine learning basicsRecommender systems
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
50