2020

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

Zhao, Wayne Xin, Mu, Shanlei, Hou, Yupeng et al.

Understand

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.

Built on

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