Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction Article Swipe
Fangye Wang
,
Hansu Gu
,
Dongsheng Li
,
Tun Lu
,
Peng Zhang
,
Ning Gu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.04635
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.04635
Click Through Rate (CTR) prediction plays an essential role in recommender systems and online advertising. It is crucial to effectively model feature interactions to improve the prediction performance of CTR models. However, existing methods face three significant challenges. First, while most methods can automatically capture high-order feature interactions, their performance tends to diminish as the order of feature interactions increases. Second, existing methods lack the ability to provide convincing interpretations of the prediction results, especially for high-order feature interactions, which limits the trustworthiness of their predictions. Third, many methods suffer from the presence of redundant parameters, particularly in the embedding layer. This paper proposes a novel method called Gated Deep Cross Network (GDCN) and a Field-level Dimension Optimization (FDO) approach to address these challenges. As the core structure of GDCN, Gated Cross Network (GCN) captures explicit high-order feature interactions and dynamically filters important interactions with an information gate in each order. Additionally, we use the FDO approach to learn condensed dimensions for each field based on their importance. Comprehensive experiments on five datasets demonstrate the effectiveness, superiority and interpretability of GDCN. Moreover, we verify the effectiveness of FDO in learning various dimensions and reducing model parameters. The code is available on \url{https://github.com/anonctr/GDCN}.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.04635
- https://arxiv.org/pdf/2311.04635
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388555705
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388555705Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2311.04635Digital Object Identifier
- Title
-
Towards Deeper, Lighter and Interpretable Cross Network for CTR PredictionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-08Full publication date if available
- Authors
-
Fangye Wang, Hansu Gu, Dongsheng Li, Tun Lu, Peng Zhang, Ning GuList of authors in order
- Landing page
-
https://arxiv.org/abs/2311.04635Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2311.04635Direct 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/2311.04635Direct OA link when available
- Concepts
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Interpretability, Computer science, Feature (linguistics), Field (mathematics), Machine learning, Artificial intelligence, Code (set theory), Data mining, Mathematics, Linguistics, Philosophy, Pure mathematics, Programming language, Set (abstract data type)Top 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.information | 146 |
| abstract_inverted_index.parameters, | 95 |
| abstract_inverted_index.parameters. | 195 |
| abstract_inverted_index.performance | 27, 49 |
| abstract_inverted_index.recommender | 10 |
| abstract_inverted_index.significant | 36 |
| abstract_inverted_index.superiority | 176 |
| abstract_inverted_index.Optimization | 117 |
| abstract_inverted_index.advertising. | 14 |
| abstract_inverted_index.interactions | 22, 58, 138, 143 |
| abstract_inverted_index.particularly | 96 |
| abstract_inverted_index.predictions. | 85 |
| abstract_inverted_index.Additionally, | 151 |
| abstract_inverted_index.Comprehensive | 168 |
| abstract_inverted_index.automatically | 43 |
| abstract_inverted_index.effectiveness | 185 |
| abstract_inverted_index.interactions, | 47, 78 |
| abstract_inverted_index.effectiveness, | 175 |
| abstract_inverted_index.interpretations | 69 |
| abstract_inverted_index.trustworthiness | 82 |
| abstract_inverted_index.interpretability | 178 |
| abstract_inverted_index.\url{https://github.com/anonctr/GDCN}. | 201 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |