Hybrid Recommender Model Results on Netflix Prize Test Set

5.5 KB of experimental results for a proposed hybrid recommender model. The dataset contains performance metrics, including a Root-Mean-Square Error (RMSE) of 0.9966 and a Mean Absolute Error (MAE) of 0.7968, from testing on the Netflix Prize dataset. It was authored by T. Keerthika and last updated on 2026-04 13.

Use Cases

• Benchmarking hybrid recommender system performance based on reported RMSE and MAE metrics.
• Studying the trade-off between predictive accuracy and interpretability based on the described model framework.
• Comparing computational efficiency against complex deep learning baselines as described in the methodology.
• Analyzing the effect of Louvain community identification and SVD on sparse data matrices.

Strengths

• Specific performance metrics are provided: RMSE of 0.9966 and MAE of 0.7968.
• The model's design for interpretability and lower computational cost is explicitly described.
• Dataset is small (5.5 KB), facilitating quick inspection and download.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small (5.5 KB), indicating limited scope, likely containing only summary results.

Provenance

Source

T. Keerthika via figshare.

Collection Method

Results from experimental analysis of a proposed hybrid model on the Netflix Prize dataset.

Time Range

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Freshness

Last updated 2026-04-13 17:28:01.

Geography

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