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20 April 2024 |
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Article overview
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RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms | | Wayne Xin Zhao
; Shanlei Mu
; Yupeng Hou
; Zihan Lin
; Kaiyuan Li
; Yushuo Chen
; Yujie Lu
; Hui Wang
; Changxin Tian
; Xingyu Pan
; Yingqian Min
; Zhichao Feng
; Xinyan Fan
; Xu Chen
; Pengfei Wang
; Wendi Ji
; Yaliang Li
; Xiaoling Wang
; Ji-Rong Wen
; | | Date: |
3 Nov 2020 | | Abstract: | In recent years, there are a large number of recommendation algorithms
proposed in the literature, from traditional collaborative filtering to neural
network 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 recommender systems for research purpose. In
this library, we implement 53 recommendation models on 27 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 this https URL | | Source: | arXiv, 2011.01731 | | Services: | Forum | Review | PDF | Favorites | |
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