{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"RecBole provides a unified framework for implementing, benchmarking, and comparing recommendation algorithms across general, sequential, context-aware, and knowledge-based categories.","skillfed_tags":["recommendation-systems","benchmarking","pytorch"],"use_cases":["Reproduce published recommendation algorithm results on standard benchmarks to verify claims or understand baselines.","Implement and test a new recommendation algorithm within a unified data and evaluation framework.","Compare multiple recommendation approaches on the same dataset using standardized metrics and train/test splits.","Preprocess and format raw recommendation data into RecBole's unified structure for consistent downstream use.","Tune hyperparameters across multiple recommendation models using built-in search and parallel evaluation."],"what_it_does":"RecBole is a research-focused recommendation library built on PyTorch that bundles a large collection of recommendation algorithms and preprocessed benchmark datasets into a single framework. It abstracts away data loading, evaluation protocols, and model training boilerplate so researchers can focus on algorithm development and comparison. The library covers four recommendation task categories: general (e.g., collaborative filtering), sequential (e.g., next-item prediction), context-aware (e.g., factorization machines), and knowledge-based (e.g., knowledge graph embeddings).\n\nThe package depends on 16 runtime libraries including torch, numpy, scipy, pandas, scikit-learn, and tensorboard. It is designed for GPU-accelerated training and includes utilities for hyperparameter tuning, significance testing, and standardized evaluation. The framework is intended for reproducible research rather than production deployment, with emphasis on benchmarking and comparing algorithms on common datasets.","worth_installing":"Yes, if you are conducting recommendation research and need a unified benchmark framework. The library is actively maintained, permissively licensed, and has low install friction. However, it is research-focused and not designed for production systems; the aging maintenance status and large dependency footprint (16 runtime packages) mean you should verify that the algorithm coverage and dataset versions match your specific research needs before committing."},"id":"recbole","links":{"html":"https://skillfed.io/packages/recbole","md":"https://skillfed.io/packages/recbole.md","pypi":"https://pypi.org/project/recbole/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-02-24","license_spdx":null,"license_treatment":"permissive","name":"recbole","python_support":"unspecified","summary":"A unified, comprehensive and efficient recommendation library"},"popularity":{"monthly_downloads":90548,"position":13581,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.2.1"}