Recommender System Algorithm Training Data Requirements Analysis

Martha Larson of Radboud University Nijmegen argues for the principle of minimal necessary data in recommender systems. The paper illustrates the trade-off between training data volume and algorithm accuracy through a set of classic recommender system experiments. It concludes that adopting training data requirements analysis is a step towards more responsible recommendation.

Use Cases

• Benchmarking recommender algorithm efficiency based on the volume of user data required
• Analyzing the trade-off between training data volume and recommendation accuracy
• Evaluating algorithms for cold-start performance based on minimal data requirements
• Developing privacy-preserving recommender systems based on the minimal necessary data principle

Strengths

• Paper advocates for a specific, actionable best practice (training data requirements analysis) for the field.
• The principle of minimal necessary data addresses multiple concerns: privacy, training time, and cold-start problems.

Limitations

• The specific experimental data, column-level documentation, and dataset size are unknown.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Radboud University Nijmegen

Collection Method

The paper describes a set of classic recommender system experiments to illustrate data-accuracy trade-offs.