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ReChorus2.0: A Modular and Task-Flexible Recommendation Library

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Shaoping MaAuthors Info & Claims

RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems

Pages 454 - 464

https://doi.org/10.1145/3640457.3688076

Published: 08 October 2024 Publication History

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Abstract

With the applications of recommendation systems rapidly expanding, an increasing number of studies have focused on every aspect of recommender systems with different data inputs, models, and task settings. Therefore, a flexible library is needed to help researchers implement the experimental strategies they require. Existing open libraries for recommendation scenarios have enabled reproducing various recommendation methods and provided standard implementations. However, these libraries often impose certain restrictions on data and seldom support the same model to perform different tasks and input formats, limiting users from customized explorations. To fill the gap, we propose ReChorus2.0, a modular and task-flexible library for recommendation researchers. Based on ReChorus, we upgrade the supported input formats, models, and training&evaluation strategies to help realize more recommendation tasks with more data types. The main contributions of ReChorus2.0 include: (1) Realization of complex and practical tasks, including re-ranking and CTR prediction tasks; (2) Inclusion of various context-aware and re-ranking recommenders; (3) Extension of existing and new models to support different tasks with the same models; (4) Support of highly-customized input with impression logs, negative items, or click labels, as well as user, item, and situation contexts. To summarize, ReChorus2.0 serves as a comprehensive and flexible library that better addresses the practical problems in the recommendation scenario and caters to more diverse research needs. The implementation and detailed tutorials of ReChorus2.0 can be found at https://github.com/THUwangcy/ReChorus.

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Cited By

Huang NDing HHu RJiao PZhao ZYang BZheng Q(2025)Multi-time-scale with clockwork recurrent neural network modeling for sequential recommendationThe Journal of Supercomputing10.1007/s11227-025-06925-481:2Online publication date: 21-Jan-2025
https://doi.org/10.1007/s11227-025-06925-4

Index Terms

ReChorus2.0: A Modular and Task-Flexible Recommendation Library
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
2. Software and its engineering
  1. Software notations and tools
    1. Software libraries and repositories

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cover image ACM Conferences

RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems

October 2024

1438 pages

ISBN:9798400705052

DOI:10.1145/3640457

Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 08 October 2024

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the Natural Science Foundation of China
Quan Cheng Laboratory

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RecSys '24

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RecSys '24: 18th ACM Conference on Recommender Systems

October 14 - 18, 2024

Bari, Italy

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Huang NDing HHu RJiao PZhao ZYang BZheng Q(2025)Multi-time-scale with clockwork recurrent neural network modeling for sequential recommendationThe Journal of Supercomputing10.1007/s11227-025-06925-481:2Online publication date: 21-Jan-2025
https://doi.org/10.1007/s11227-025-06925-4

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