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RecStudio: Towards a Highly-Modularized Recommender System

Authors:

Enhong ChenAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2890 - 2900

https://doi.org/10.1145/3539618.3591894

Published: 18 July 2023 Publication History

Abstract

A dozen recommendation libraries have recently been developed to accommodate popular recommendation algorithms for reproducibility. However, they are almost simply a collection of algorithms, overlooking the modularization of recommendation algorithms and their usage in practical scenarios. Algorithmic modularization has the following advantages: 1) helps to understand the effectiveness of each algorithm; 2) easily assembles new algorithms with well-performed modules by either drag-and-drop programming or automatic machine learning; 3) enables reinforcement between algorithms since one algorithm may act as a module of another algorithm. To this end, we develop a highly-modularized recommender system -- RecStudio, in which any recommendation algorithm is categorized into either a ranker or a retriever. In the RecStudio library, we implement 90 recommendation algorithms with the pure Pytorch, covering both common algorithms in other libraries and complex algorithms involving multiple recommendation models. RecStudio is featured from several perspectives, such as index-supported efficient recommendation and evaluation, GPU-accelerated negative sampling, hyperparameter learning on the validation, and cooperation between the retriever and ranker. RecStudio is also equipped with a web service, where the recommendation pipeline can be quickly established and visually evaluated on selected datasets, and the evaluation results are automatically archived and visualized in a leaderboard. The project and documents are released at http://recstudio.org.cn.

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Cited By

Yin MWang HXu XWu LZhao SGuo WLiu YTang RLian DChen EFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614781(3009-3019)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614781

Index Terms

RecStudio: Towards a Highly-Modularized Recommender System
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

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Published: 18 July 2023

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the National Natural Science Foundation of China
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SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Cited By

Yin MWang HXu XWu LZhao SGuo WLiu YTang RLian DChen EFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614781(3009-3019)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614781

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