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

@article{Zhao2021RecBoleTA,
  title={RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms},
  author={Wayne Xin Zhao and Shanlei Mu and Yupeng Hou and Zihan Lin and Kaiyuan Li and Yushuo Chen and Yujie Lu and Haibo Wang and Changxin Tian and Xingyu Pan and Yingqian Min and Zhichao Feng and Xinyan Fan and Xu Chen and Pengfei Wang and Wendi Ji and Yaliang Li and Xiaoling Wang and Ji-rong Wen},
  journal={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
  year={2021}
}
  • Wayne Xin Zhao, Shanlei Mu, Ji-rong Wen
  • Published 3 November 2020
  • Computer Science
  • Proceedings of the 30th ACM International Conference on Information & Knowledge Management
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 (pronounced as [rEk'boUl@r]), which provides a unified鈥β

Figures and Tables from this paper

RecBole 2.0: Towards a More Up-to-Date Recommendation Library

An extended recommendation library consisting of eight packages for up-to-date topics and architectures based on a popular recommendation framework RecBole, ensuring that both the implementation and interface are unified.

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

A gentle introduction to recommendation systems is provided, describing the task they are designed to solve and the challenges faced in research, and an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information.

Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

Elliot is a comprehensive recommendation framework that aims to run and reproduce an entire experimental pipeline by processing a simple configuration file and optimizes hyperparameters for several recommendation algorithms.

A Revisiting Study of Appropriate Offline Evaluation for Top-N Recommendation Algorithms

This work presents a large-scale, systematic study on six important factors from three aspects for evaluating recommender systems, and provides several suggested settings that are specially important for performance comparison.

DeepCARSKit: A Demo and User Guide

The DeepCARSKit library is released, it provides a unified platform for implementing and evaluating context-aware recommendation models based on neural networks, and a user guide to help better use and evaluate the library.

Self-Supervised Learning for Recommender Systems: A Survey

An exclusive definition of SSR is proposed, on top of which a comprehensive taxonomy is built to divide existing SSR methods into four categories: contrastive, generative, predictive, and hybrid.

Towards Universal Sequence Representation Learning for Recommender Systems

A novel universal sequence representation learning approach that utilizes the associated description text of items to learn transferable representations across different recommendation scenarios, and leads to a performance improvement in a cross-platform setting, showing the strong transferability of the proposed universal SRL method.

Filter-enhanced MLP is All You Need for Sequential Recommendation

The all-MLP architecture endows the model with lower time complexity, and the learnable filters can adaptively attenuate the noise information in the frequency domain, which demonstrates the superiority of the proposed method over competitive RNN, CNN, GNN and Transformer-based methods.

Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation

Ada-Ranker can adaptively modulate parameters of a ranker according to the data distribution of the current group of item candidates and can effectively enhance various base sequential models and also outperform a comprehensive set of competitive baselines.

iRec: An Interactive Recommendation Framework

This work proposes an interactive RS framework named iRec, which covers the whole experimentation process by following the main RS guidelines and contains several state-of-the-art algorithms, a hyperparameter tuning module, distinct evaluation metrics, different ways of visualizing the results, and statistical validation.
...

References

SHOWING 1-10 OF 108 REFERENCES

BETA-Rec: Build, Evaluate and Tune Automated Recommender Systems

BETA-Rec, an open source project for Building, Evaluating and Tuning Automated Recommender Systems, aims to provide a practical data toolkit for building end-to-end recommendation systems in a standardized way and is designed to be both modular and extensible.

Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

Elliot is a comprehensive recommendation framework that aims to run and reproduce an entire experimental pipeline by processing a simple configuration file and optimizes hyperparameters for several recommendation algorithms.

Deep Learning Based Recommender System

A taxonomy of deep learning-based recommendation models is provided and a comprehensive summary of the state of the art is provided, along with new perspectives pertaining to this new and exciting development of the field.

RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation

Recurrent neural networks for session-based recommendation have attracted a lot of attention recently because of their promising performance. repeat consumption is a common phenomenon in many

Collaborative Denoising Auto-Encoders for Top-N Recommender Systems

It is demonstrated that the proposed model is a generalization of several well-known collaborative filtering models but with more flexible components, and that CDAE consistently outperforms state-of-the-art top-N recommendation methods on a variety of common evaluation metrics.

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

A novel Compressed Interaction Network (CIN), which aims to generate feature interactions in an explicit fashion and at the vector-wise level and is named eXtreme Deep Factorization Machine (xDeepFM), which is able to learn certain bounded-degree feature interactions explicitly and can learn arbitrary low- and high-order feature interactions implicitly.

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

This work proposes a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering, and is much easier to implement and train, exhibiting substantial improvements over Neural Graph Collaborative Filtering (NGCF) under exactly the same experimental setting.

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

This work proposes a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items.

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

This paper shows that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions, and combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.

Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

This paper considers knowledge graphs as the source of side information and proposes MKR, a Multi-task feature learning approach for Knowledge graph enhanced Recommendation, a deep end-to-end framework that utilizes knowledge graph embedding task to assist recommendation task.
...