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

Meng, Z. et al. (2020) BETA-Rec: Build, Evaluate and Tune Automated Recommender Systems. In: 14th ACM Conference on Recommender Systems (RecSys 2020), 22-26 Sep 2020, pp. 588-590. ISBN 9781450375832 (doi: 10.1145/3383313.3411524)

Abstract

The field of recommender systems has rapidly evolved over the last few years, with significant advances made due to the in-flux of deep learning techniques. However, as a result of this rapid progress, escalating barriers-to-entry for new researchers is emerging. In particular, state-of-the-art approaches have fragmented into a large number of code-bases, often requiring different input formats, pre-processing stages and evaluating with different metric packages. Hence, it is time-consuming for new researchers to reach the point of having both an effective baseline set and a sound comparative environment. As a step towards elevating this problem, we have developed BETA-Rec, an open source project for Building, Evaluating and Tuning Automated Recommender Systems. BETA-Rec aims to provide a practical data toolkit for building end-to-end recommendation systems in a standardized way. It provides means for dataset preparation and splitting using common strategies, a generalized model engine for implementing recommender models using Pytorch with 9 models available out-of-the-box, as well as a unified training, validation, tuning and testing pipeline. Furthermore, BETA-Rec is designed to be both modular and extensible, enabling new models to be quickly added to the framework. It is deployable in a wide range of environments via pre-built docker containers and supports distributed parameter tuning using Ray. In this demo, we will illustrate the deployment and use of BETA-Rec for researchers and practitioners on a number of standard recommendation datasets. The source code of the project is available at github: https://github.com/beta-team/beta-recsys.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Mccreadie, Dr Richard and Meng, Dr Zaiqiao and Liu, Siwei and Macdonald, Professor Craig and Wu, Mr Yaxiong and Ounis, Professor Iadh
• Authors: Meng, Z., McCreadie, R., Macdonald, C., Ounis, I., Liang, S., Liu, S., Zeng, G., Junha, L., Liang, Y., Zhang, Q., and Wu, Y.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450375832
• Copyright Holders: Copyright © 2020 The Authors
• First Published: First published in 14th ACM Conference on Recommender Systems (RecSys 2020): 588-590
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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• Organisation

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
300332		BigDataStack	Iadh Ounis	European Commission (EC)	779747	Computing Science