LiMAML: Personalization of Deep Recommender Models via Meta Learning Article Swipe
Ruofan Wang
,
Prakruthi Prabhakar
,
Gaurav Srivastava
,
Tianqi Wang
,
Zeinab S. Jalali
,
Varun Bharill
,
Yunbo Ouyang
,
Aastha Nigam
,
Divya Venugopalan
,
Aman Gupta
,
Fedor Borisyuk
,
Sathiya Keerthi
,
Ajith Muralidharan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.00803
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.00803
In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases continue to expand, the necessity of personalization and frequent model updates have assumed paramount significance to ensure the delivery of relevant and refreshed experiences to a diverse array of members. In this work, we introduce an innovative meta-learning solution tailored to the personalization of models for individual members and other entities, coupled with the frequent updates based on the latest user interaction signals. Specifically, we leverage the Model-Agnostic Meta Learning (MAML) algorithm to adapt per-task sub-networks using recent user interaction data. Given the near infeasibility of productionizing original MAML-based models in online recommendation systems, we propose an efficient strategy to operationalize meta-learned sub-networks in production, which involves transforming them into fixed-sized vectors, termed meta embeddings, thereby enabling the seamless deployment of models with hundreds of billions of parameters for online serving. Through extensive experimentation on production data drawn from various applications at LinkedIn, we demonstrate that the proposed solution consistently outperforms the baseline models of those applications, including strong baselines such as using wide-and-deep ID based personalization approach. Our approach has enabled the deployment of a range of highly personalized AI models across diverse LinkedIn applications, leading to substantial improvements in business metrics as well as refreshed experience for our members.
Related Topics
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2403.00803
- https://arxiv.org/pdf/2403.00803
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392489948
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4392489948Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2403.00803Digital Object Identifier
- Title
-
LiMAML: Personalization of Deep Recommender Models via Meta LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-23Full publication date if available
- Authors
-
Ruofan Wang, Prakruthi Prabhakar, Gaurav Srivastava, Tianqi Wang, Zeinab S. Jalali, Varun Bharill, Yunbo Ouyang, Aastha Nigam, Divya Venugopalan, Aman Gupta, Fedor Borisyuk, Sathiya Keerthi, Ajith MuralidharanList of authors in order
- Landing page
-
https://arxiv.org/abs/2403.00803Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2403.00803Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2403.00803Direct OA link when available
- Concepts
-
Personalization, Recommender system, Computer science, Artificial intelligence, World Wide Web, Information retrieval, Machine learning, Data scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.leverage | 92 |
| abstract_inverted_index.members. | 56, 227 |
| abstract_inverted_index.modeling | 20 |
| abstract_inverted_index.networks | 12 |
| abstract_inverted_index.original | 114 |
| abstract_inverted_index.paradigm | 18 |
| abstract_inverted_index.per-task | 101 |
| abstract_inverted_index.proposed | 174 |
| abstract_inverted_index.relevant | 47 |
| abstract_inverted_index.seamless | 145 |
| abstract_inverted_index.serving. | 157 |
| abstract_inverted_index.signals. | 89 |
| abstract_inverted_index.solution | 65, 175 |
| abstract_inverted_index.strategy | 125 |
| abstract_inverted_index.systems, | 5, 120 |
| abstract_inverted_index.tailored | 66 |
| abstract_inverted_index.vectors, | 138 |
| abstract_inverted_index.LinkedIn, | 169 |
| abstract_inverted_index.algorithm | 98 |
| abstract_inverted_index.approach. | 194 |
| abstract_inverted_index.baselines | 186 |
| abstract_inverted_index.efficient | 124 |
| abstract_inverted_index.entities, | 77 |
| abstract_inverted_index.extensive | 159 |
| abstract_inverted_index.including | 184 |
| abstract_inverted_index.introduce | 61 |
| abstract_inverted_index.necessity | 31 |
| abstract_inverted_index.paramount | 40 |
| abstract_inverted_index.refreshed | 49, 223 |
| abstract_inverted_index.MAML-based | 115 |
| abstract_inverted_index.deployment | 146, 200 |
| abstract_inverted_index.experience | 224 |
| abstract_inverted_index.individual | 73 |
| abstract_inverted_index.innovative | 63 |
| abstract_inverted_index.parameters | 154 |
| abstract_inverted_index.production | 162 |
| abstract_inverted_index.ubiquitous | 7 |
| abstract_inverted_index.demonstrate | 171 |
| abstract_inverted_index.embeddings, | 141 |
| abstract_inverted_index.experiences | 50 |
| abstract_inverted_index.fixed-sized | 137 |
| abstract_inverted_index.interaction | 88, 106 |
| abstract_inverted_index.objectives. | 23 |
| abstract_inverted_index.outperforms | 177 |
| abstract_inverted_index.production, | 131 |
| abstract_inverted_index.recommender | 4 |
| abstract_inverted_index.substantial | 215 |
| abstract_inverted_index.applications | 167 |
| abstract_inverted_index.consistently | 176 |
| abstract_inverted_index.improvements | 216 |
| abstract_inverted_index.meta-learned | 128 |
| abstract_inverted_index.personalized | 206 |
| abstract_inverted_index.significance | 41 |
| abstract_inverted_index.sub-networks | 102, 129 |
| abstract_inverted_index.transforming | 134 |
| abstract_inverted_index.Specifically, | 90 |
| abstract_inverted_index.applications, | 183, 212 |
| abstract_inverted_index.infeasibility | 111 |
| abstract_inverted_index.meta-learning | 64 |
| abstract_inverted_index.wide-and-deep | 190 |
| abstract_inverted_index.Model-Agnostic | 94 |
| abstract_inverted_index.operationalize | 127 |
| abstract_inverted_index.recommendation | 119 |
| abstract_inverted_index.experimentation | 160 |
| abstract_inverted_index.personalization | 33, 69, 193 |
| abstract_inverted_index.productionizing | 113 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 13 |
| citation_normalized_percentile |