udata-recommendations 3.1.6

uData content recommendations bridge

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

3.1.6 (2024-07-30)

• Migrate to Python 3.11 following udata dependencies upgrade #265
• Unpin jsonschema deps #266

3.1.5 (2023-11-21)

• Update Matomo content tracking data-attributes #263
• Upgrade test and develop dependencies #264

3.1.4 (2023-03-07)

• Update and compile translations #261 #262

3.1.3 (2023-03-02)

• Recommendations for new dataset page #256

3.1.2 (2022-12-15)

• Update dataset and reuse recommendations to match new udata-front layout 207

3.1.1 (2022-09-01)

• Replace mongo legacy image in CI #226
• Store unique recommendations in extras #239

3.1.0 (2021-09-16)

• Change udata-gouvfr dependency to udata-front following renaming #188

3.0.0 (2021-08-12)

• Ensure compatibility with udata3 by changing imports and style #183

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