Skip to main content

uData content recommendations bridge

Project description

udata-recommendations

This plugin acts as a bridge between uData and a recommendation system.

In our case (data.gouv.fr), it's a set of scripts living here https://github.com/etalab/piwik-covisits.

Recommendations are stored on datasets. Recommendations can come from various sources and are stored in a descending order, according to the provided score (from 1 to 100). The top recommendations are displayed at the bottom on the dataset page.

Compatibility

udata-recommendations requires Python 3.7+ and uData.

Installation

Install uData.

Remain in the same virtual environment (for Python).

Install udata-recommendations:

pip install udata-recommendations

Modify your local configuration file of udata (typically, udata.cfg) as following:

PLUGINS = ['recommendations']
RECOMMENDATIONS_SOURCES = {
    'source-name': 'https://path/to/recommendations.json',
    'other-source': 'https://path/to/other/recommendations.json',
}
RECOMMENDATIONS_NB_RECOMMENDATIONS = 4
  • RECOMMENDATIONS_SOURCES: A key-value dictionary of recommendation sources and URLs to fetch. Default: {}
  • RECOMMENDATIONS_NB_RECOMMENDATIONS: The maximum number of recommendations to display on the dataset page. Default: 4

Usage

Adding recommendations

You can fetch and store recommendations as a task, using your configuration in RECOMMENDATIONS_SOURCES, on a schedule if needed. By default, previous recommendations are cleaned before the importing new ones, but you're in control.

udata job run recommendations-add
# Don't clean each source before importing new recommendations
udata job run recommendations-add should_clean=false

Deleting recommendations

To clean all recommendations, you can run the following task.

udata job run recommendations-clean

Expectations

This plugin expects the following format to provide datasets recommendations:

[
  {
    "id": "dataset-id",
    "recommendations": [
      {
        "id": "dataset-slug-1",
        "score": 100
      },
      {
        "id": "5ef1fe80f50446b8f41ba691",
        "score": 1
      }
    ]
  },
  {
    "id": "dataset-id2",
    "recommendations": [
      {
        "id": "5ef1fe80f50446b8f41ba691",
        "score": 50
      }
    ]
  }
]

Dataset IDs can be IDs or slugs. Scores should be between 1 and 100, inclusive. You can validate your JSON using a JSON Schema.

Changelog

2.2.0 (2020-11-30)

  • Add reuses support #153

2.1.1 (2020-10-16)

  • Ignore recommendation of dataset itself #147

2.1.0 (2020-08-25)

  • Add score to recommendations and support multiple recommendation sources #142

2.0.0 (2020-03-11)

  • udata 2.0 / Python 3 support #95
  • Support new hooks format #96

1.0.1 (2018-08-03)

  • Nothing yet

1.0.0 (2018-06-06)

  • Allow slug instead of id for datasets #8
  • Initial release

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for udata-recommendations, version 2.2.0
Filename, size File type Python version Upload date Hashes
Filename, size udata_recommendations-2.2.0-py2.py3-none-any.whl (10.5 kB) File type Wheel Python version py2.py3 Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page