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Recsys Pipeline

Recsys Pipeline is an open-source Python example that demonstrates an end-to-end MovieLens recommender workflow, from offline preprocessing and model training to term and vector retrieval, ranking, inference, and a Flask web application.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Recsys Pipeline — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to inspect or adapt a documented recommender-system workflow built around MovieLens, rather than simply call a hosted recommendation API.

Who it is for

  • Developers learning how recommender-system components connect from offline processing to online serving.
  • Teams prototyping a recommendation workflow with retrieval, feature storage, model ranking, and inference components.

Documented uses

  • Prepare MovieLens ratings and interaction history for offline training and evaluation.
  • Generate term-based and vector-based recommendation candidates.
  • Train a ranking model and connect ranking features to inference components.
  • Study a local example that combines a Flask web server with containerized supporting services.
Before you choose

Requirements & limits

The project materials are in a public repository described as open source. The README documents local Conda and Docker setup.

  • The example uses MovieLens and converts ratings into implicit-feedback labels for its workflow.
  • The ranking implementation is described as a sparse FM module combined with a dense MLP; despite the DeepFM label, it is not a conventional DeepFM structure.
  • The documented stack separates offline Python work in Conda from Redis, Elasticsearch, Feast, and Triton services in Docker.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the documented offline Conda environment

Create the Python environment used for offline preprocessing and model training.

Before you start

  • Conda
  • The repository checkout, including requirements.txt
  • Internet access to install the listed packages

Steps

  1. Create a Conda environment with Python 3.8 using the README's command.

  2. Activate the rsppl environment.

  3. Install packages from requirements.txt using the Anaconda and conda-forge channels.

Expected outcome

A Conda environment prepared for the repository's offline data processing and model training stages.

Guide limitations

  • This setup guide does not cover running preprocessing, training, Docker services, or the web application.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
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Sources checked
18 October 2026
Original website title
akiragy/recsys_pipeline · Official homepage title · retained in its source language
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