RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

Abstract

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation algorithms continually increase in the research community. In the light of this challenge, we propose a unified, comprehensive and efficient recommender system library called RecBole, which provides a unified framework to develop and reproduce recommendation algorithms for research purpose. In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation. We implement the RecBole library based on PyTorch, which is one of the most popular deep learning frameworks. Our library is featured in many aspects, including general and extensible data structures, comprehensive benchmark models and datasets, efficient GPU-accelerated execution, and extensive and standard evaluation protocols. We provide a series of auxiliary functions, tools, and scripts to facilitate the use of this library, such as automatic parameter tuning and break-point resume. Such a framework is useful to standardize the implementation and evaluation of recommender systems. The project and documents are released at https://recbole.io/.

arXiv comment12 pages, 4 figures

References in corpus (14)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• KGAT: Knowledge Graph Attention Network for Recommendation
• Graph Convolutional Matrix Completion
• xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• NAIS: Neural Attentive Item Similarity Model for Recommendation
• Learning over Knowledge-Base Embeddings for Recommendation
• Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
• Translation-based Recommendation
• Embarrassingly Shallow Autoencoders for Sparse Data
• Metadata Embeddings for User and Item Cold-start Recommendations
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Cited by in corpus (9)

• Modeling Two-Way Selection Preference for Person-Job Fit
• Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?
• It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic
• SIGIR 2021 E-Commerce Workshop Data Challenge
• A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing
• KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?
• Top-N Recommendation with Counterfactual User Preference Simulation
• Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning
• Hyperbolic Hypergraphs for Sequential Recommendation