RecFlow: An Industrial Full Flow Recommendation Dataset Article Swipe
Qi Liu
,
Kai Zheng
,
Rui Huang
,
Weihua Li
,
Kuo Cai
,
Yi Chai
,
Yanan Niu
,
Yiqun Hui
,
Bing Han
,
Na Mou
,
Hongning Wang
,
Wentian Bao
,
Yunen Yu
,
Guorui Zhou
,
Han Li
,
Yang Song
,
Defu Lian
,
Kun Gai
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.20868
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.20868
Industrial recommendation systems (RS) rely on the multi-stage pipeline to balance effectiveness and efficiency when delivering items from a vast corpus to users. Existing RS benchmark datasets primarily focus on the exposure space, where novel RS algorithms are trained and evaluated. However, when these algorithms transition to real world industrial RS, they face a critical challenge of handling unexposed items which are a significantly larger space than the exposed one. This discrepancy profoundly impacts their practical performance. Additionally, these algorithms often overlook the intricate interplay between multiple RS stages, resulting in suboptimal overall system performance. To address this issue, we introduce RecFlow, an industrial full flow recommendation dataset designed to bridge the gap between offline RS benchmarks and the real online environment. Unlike existing datasets, RecFlow includes samples not only from the exposure space but also unexposed items filtered at each stage of the RS funnel. Our dataset comprises 38M interactions from 42K users across nearly 9M items with additional 1.9B stage samples collected from 9.3M online requests over 37 days and spanning 6 stages. Leveraging the RecFlow dataset, we conduct courageous exploration experiments, showcasing its potential in designing new algorithms to enhance effectiveness by incorporating stage-specific samples. Some of these algorithms have already been deployed online, consistently yielding significant gains. We propose RecFlow as the first comprehensive benchmark dataset for the RS community, supporting research on designing algorithms at any stage, study of selection bias, debiased algorithms, multi-stage consistency and optimality, multi-task recommendation, and user behavior modeling. The RecFlow dataset, along with the corresponding source code, is available at https://github.com/RecFlow-ICLR/RecFlow.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.20868
- https://arxiv.org/pdf/2410.20868
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404314622
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404314622Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2410.20868Digital Object Identifier
- Title
-
RecFlow: An Industrial Full Flow Recommendation DatasetWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-28Full publication date if available
- Authors
-
Qi Liu, Kai Zheng, Rui Huang, Weihua Li, Kuo Cai, Yi Chai, Yanan Niu, Yiqun Hui, Bing Han, Na Mou, Hongning Wang, Wentian Bao, Yunen Yu, Guorui Zhou, Han Li, Yang Song, Defu Lian, Kun GaiList of authors in order
- Landing page
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https://arxiv.org/abs/2410.20868Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2410.20868Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2410.20868Direct OA link when available
- Concepts
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Flow (mathematics), Computer science, Information retrieval, Mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.trained | 38 |
| abstract_inverted_index.Existing | 23 |
| abstract_inverted_index.However, | 41 |
| abstract_inverted_index.RecFlow, | 101 |
| abstract_inverted_index.behavior | 246 |
| abstract_inverted_index.critical | 54 |
| abstract_inverted_index.dataset, | 178, 250 |
| abstract_inverted_index.datasets | 26 |
| abstract_inverted_index.debiased | 236 |
| abstract_inverted_index.deployed | 205 |
| abstract_inverted_index.designed | 108 |
| abstract_inverted_index.existing | 123 |
| abstract_inverted_index.exposure | 31, 132 |
| abstract_inverted_index.filtered | 138 |
| abstract_inverted_index.handling | 57 |
| abstract_inverted_index.includes | 126 |
| abstract_inverted_index.multiple | 86 |
| abstract_inverted_index.overlook | 81 |
| abstract_inverted_index.pipeline | 8 |
| abstract_inverted_index.requests | 167 |
| abstract_inverted_index.research | 225 |
| abstract_inverted_index.samples. | 197 |
| abstract_inverted_index.spanning | 172 |
| abstract_inverted_index.yielding | 208 |
| abstract_inverted_index.available | 258 |
| abstract_inverted_index.benchmark | 25, 218 |
| abstract_inverted_index.challenge | 55 |
| abstract_inverted_index.collected | 163 |
| abstract_inverted_index.comprises | 148 |
| abstract_inverted_index.datasets, | 124 |
| abstract_inverted_index.designing | 188, 227 |
| abstract_inverted_index.interplay | 84 |
| abstract_inverted_index.intricate | 83 |
| abstract_inverted_index.introduce | 100 |
| abstract_inverted_index.modeling. | 247 |
| abstract_inverted_index.potential | 186 |
| abstract_inverted_index.practical | 75 |
| abstract_inverted_index.primarily | 27 |
| abstract_inverted_index.resulting | 89 |
| abstract_inverted_index.selection | 234 |
| abstract_inverted_index.unexposed | 58, 136 |
| abstract_inverted_index.Industrial | 0 |
| abstract_inverted_index.Leveraging | 175 |
| abstract_inverted_index.additional | 159 |
| abstract_inverted_index.algorithms | 36, 44, 79, 190, 201, 228 |
| abstract_inverted_index.benchmarks | 116 |
| abstract_inverted_index.community, | 223 |
| abstract_inverted_index.courageous | 181 |
| abstract_inverted_index.delivering | 15 |
| abstract_inverted_index.efficiency | 13 |
| abstract_inverted_index.evaluated. | 40 |
| abstract_inverted_index.industrial | 49, 103 |
| abstract_inverted_index.multi-task | 242 |
| abstract_inverted_index.profoundly | 72 |
| abstract_inverted_index.showcasing | 184 |
| abstract_inverted_index.suboptimal | 91 |
| abstract_inverted_index.supporting | 224 |
| abstract_inverted_index.transition | 45 |
| abstract_inverted_index.algorithms, | 237 |
| abstract_inverted_index.consistency | 239 |
| abstract_inverted_index.discrepancy | 71 |
| abstract_inverted_index.exploration | 182 |
| abstract_inverted_index.multi-stage | 7, 238 |
| abstract_inverted_index.optimality, | 241 |
| abstract_inverted_index.significant | 209 |
| abstract_inverted_index.consistently | 207 |
| abstract_inverted_index.environment. | 121 |
| abstract_inverted_index.experiments, | 183 |
| abstract_inverted_index.interactions | 150 |
| abstract_inverted_index.performance. | 76, 94 |
| abstract_inverted_index.Additionally, | 77 |
| abstract_inverted_index.comprehensive | 217 |
| abstract_inverted_index.corresponding | 254 |
| abstract_inverted_index.effectiveness | 11, 193 |
| abstract_inverted_index.incorporating | 195 |
| abstract_inverted_index.significantly | 63 |
| abstract_inverted_index.recommendation | 1, 106 |
| abstract_inverted_index.stage-specific | 196 |
| abstract_inverted_index.recommendation, | 243 |
| abstract_inverted_index.https://github.com/RecFlow-ICLR/RecFlow. | 260 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 18 |
| citation_normalized_percentile |