Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems Article Swipe
Xiaohui Chen
,
Jiankai Sun
,
Taiqing Wang
,
Ruocheng Guo
,
Liping Liu
,
Aonan Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3580305.3599834
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3580305.3599834
Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e.g. sample hardness. However, when the pilot model is misspecified, model-based subsampling methods deteriorate. Since model misspecification is persistent in real recommendation systems, we instead propose model-agnostic data subsampling methods by only exploring input data structure represented by graphs. Specifically, we study the topology of the user-item graph to estimate the importance of each user-item interaction (an edge in the user-item graph) via graph conductance, followed by a propagation step on the network to smooth out the estimated importance value. Since our proposed method is model-agnostic, we can marry the merits of both model-agnostic and model-based subsampling methods. Empirically, we show that combing the two consistently improves over any single method on the used datasets. Experimental results on KuaiRec and MIND datasets demonstrate that our proposed methods achieve superior results compared to baseline approaches.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3580305.3599834
- https://dl.acm.org/doi/pdf/10.1145/3580305.3599834
- OA Status
- gold
- Cited By
- 2
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378711628
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- OpenAlex ID
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https://openalex.org/W4378711628Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3580305.3599834Digital Object Identifier
- Title
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Graph-Based Model-Agnostic Data Subsampling for Recommendation SystemsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-08-04Full publication date if available
- Authors
-
Xiaohui Chen, Jiankai Sun, Taiqing Wang, Ruocheng Guo, Liping Liu, Aonan ZhangList of authors in order
- Landing page
-
https://doi.org/10.1145/3580305.3599834Publisher landing page
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https://dl.acm.org/doi/pdf/10.1145/3580305.3599834Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3580305.3599834Direct OA link when available
- Concepts
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Computer science, Graph, Data mining, Data modeling, Recommender system, Artificial intelligence, Machine learning, Theoretical computer science, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2024: 1, 2023: 1Per-year citation counts (last 5 years)
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.graphs. | 69 |
| abstract_inverted_index.instead | 55 |
| abstract_inverted_index.measure | 27 |
| abstract_inverted_index.methods | 16, 43, 60, 157 |
| abstract_inverted_index.network | 103 |
| abstract_inverted_index.propose | 56 |
| abstract_inverted_index.require | 21 |
| abstract_inverted_index.results | 147, 160 |
| abstract_inverted_index.However, | 34 |
| abstract_inverted_index.baseline | 163 |
| abstract_inverted_index.compared | 161 |
| abstract_inverted_index.datasets | 152 |
| abstract_inverted_index.estimate | 80 |
| abstract_inverted_index.followed | 96 |
| abstract_inverted_index.improves | 137 |
| abstract_inverted_index.methods. | 128 |
| abstract_inverted_index.proposed | 113, 156 |
| abstract_inverted_index.superior | 159 |
| abstract_inverted_index.systems, | 53 |
| abstract_inverted_index.systems. | 13 |
| abstract_inverted_index.topology | 74 |
| abstract_inverted_index.training | 9 |
| abstract_inverted_index.datasets. | 145 |
| abstract_inverted_index.estimated | 108 |
| abstract_inverted_index.exploring | 63 |
| abstract_inverted_index.hardness. | 33 |
| abstract_inverted_index.structure | 66 |
| abstract_inverted_index.user-item | 77, 85, 91 |
| abstract_inverted_index.importance | 29, 82, 109 |
| abstract_inverted_index.persistent | 49 |
| abstract_inverted_index.approaches. | 164 |
| abstract_inverted_index.demonstrate | 153 |
| abstract_inverted_index.interaction | 86 |
| abstract_inverted_index.large-scale | 11 |
| abstract_inverted_index.model-based | 18, 41, 126 |
| abstract_inverted_index.pre-trained | 23 |
| abstract_inverted_index.propagation | 99 |
| abstract_inverted_index.represented | 67 |
| abstract_inverted_index.subsampling | 1, 15, 42, 59, 127 |
| abstract_inverted_index.Empirically, | 129 |
| abstract_inverted_index.Experimental | 146 |
| abstract_inverted_index.conductance, | 95 |
| abstract_inverted_index.consistently | 136 |
| abstract_inverted_index.deteriorate. | 44 |
| abstract_inverted_index.Specifically, | 70 |
| abstract_inverted_index.misspecified, | 40 |
| abstract_inverted_index.model-agnostic | 57, 124 |
| abstract_inverted_index.recommendation | 12, 52 |
| abstract_inverted_index.model-agnostic, | 116 |
| abstract_inverted_index.misspecification | 47 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.79199627 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |