Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3383313.3411524 | RECSYS |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zaiqiao Meng | 1 | 37 | 12.07 |
| Richard Mccreadie | 2 | 403 | 32.43 |
| Craig Macdonald | 3 | 2588 | 178.50 |
| Iadh Ounis | 4 | 3438 | 234.59 |
| Siwei Liu | 5 | 1 | 0.68 |
| Yaxiong Wu | 6 | 2 | 1.37 |
| Xi Wang | 7 | 1 | 1.02 |
| Shangsong Liang | 8 | 338 | 27.36 |
| Yucheng Liang | 9 | 1 | 0.35 |
| Guangtao Zeng | 10 | 1 | 0.35 |
| Junhua Liang | 11 | 13 | 0.89 |
| Qiang Zhang | 12 | 15 | 7.11 |