Enhancing Cross-Domain Click-Through Rate Prediction via Explicit Feature Augmentation Article Swipe
Xu Chen
,
Zida Cheng
,
Jiangchao Yao
,
Chen Ju
,
Weilin Huang
,
Jinsong Lan
,
Xiaoyi Zeng
,
Shuai Xiao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3589335.3648341
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3589335.3648341
Cross-domain CTR (CDCTR) prediction is an important research topic that studies how to leverage meaningful data from a related domain to help CTR prediction in target domain. Most existing CDCTR works design implicit ways to transfer knowledge across domains such as parameter-sharing that regularizes the model training in target domain. More effectively, recent researchers propose explicit techniques to extract user interest knowledge and transfer this knowledge to target domain. However, the proposed method mainly faces two issues: 1) it usually requires a super domain, i.e. an extremely large source domain, to cover most users or items of target domain, and 2) the extracted user interest knowledge is static no matter what the context is in target domain. These limitations motivate us to develop a more flexible and efficient technique to explicitly transfer knowledge. In this work, we propose a cross-domain augmentation network (CDAnet) being able to perform explicit knowledge transfer between two domains. Specifically, CDAnet contains a designed translation network and an augmentation network which are trained sequentially. The translation network computes latent features from two domains and learns meaningful cross-domain knowledge of each input in target domain by using a designed cross-supervised feature translator. Later the augmentation network employs the explicit cross-domain knowledge as augmented information to boost the target domain CTR prediction. Through extensive experiments on two public benchmarks and one industrial production dataset, we show CDAnet can learn meaningful translated features and largely improve the performance of CTR prediction. CDAnet has been conducted online A/B test in image2product retrieval at Taobao app, bringing an absolute 0.11 point CTR improvement, a relative 0.64% deal growth and a relative 1.26% GMV increase.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3589335.3648341
- https://dl.acm.org/doi/pdf/10.1145/3589335.3648341
- OA Status
- gold
- Cited By
- 9
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396844128
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396844128Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/3589335.3648341Digital Object Identifier
- Title
-
Enhancing Cross-Domain Click-Through Rate Prediction via Explicit Feature AugmentationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-12Full publication date if available
- Authors
-
Xu Chen, Zida Cheng, Jiangchao Yao, Chen Ju, Weilin Huang, Jinsong Lan, Xiaoyi Zeng, Shuai XiaoList of authors in order
- Landing page
-
https://doi.org/10.1145/3589335.3648341Publisher landing page
- PDF URL
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https://dl.acm.org/doi/pdf/10.1145/3589335.3648341Direct 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/10.1145/3589335.3648341Direct OA link when available
- Concepts
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Computer science, Click-through rate, Feature (linguistics), Domain (mathematical analysis), Artificial intelligence, Mathematics, Information retrieval, Linguistics, Philosophy, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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33Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.or | 94 |
| abstract_inverted_index.to | 12, 20, 34, 57, 66, 90, 121, 129, 145, 207 |
| abstract_inverted_index.us | 120 |
| abstract_inverted_index.we | 136, 226 |
| abstract_inverted_index.A/B | 247 |
| abstract_inverted_index.CTR | 1, 22, 212, 240, 260 |
| abstract_inverted_index.GMV | 271 |
| abstract_inverted_index.The | 168 |
| abstract_inverted_index.and | 62, 99, 126, 160, 177, 221, 234, 267 |
| abstract_inverted_index.are | 165 |
| abstract_inverted_index.can | 229 |
| abstract_inverted_index.has | 243 |
| abstract_inverted_index.how | 11 |
| abstract_inverted_index.one | 222 |
| abstract_inverted_index.the | 44, 70, 101, 111, 196, 200, 209, 237 |
| abstract_inverted_index.two | 75, 151, 175, 218 |
| abstract_inverted_index.0.11 | 258 |
| abstract_inverted_index.More | 50 |
| abstract_inverted_index.Most | 27 |
| abstract_inverted_index.able | 144 |
| abstract_inverted_index.app, | 254 |
| abstract_inverted_index.been | 244 |
| abstract_inverted_index.data | 15 |
| abstract_inverted_index.deal | 265 |
| abstract_inverted_index.each | 183 |
| abstract_inverted_index.from | 16, 174 |
| abstract_inverted_index.help | 21 |
| abstract_inverted_index.i.e. | 84 |
| abstract_inverted_index.more | 124 |
| abstract_inverted_index.most | 92 |
| abstract_inverted_index.show | 227 |
| abstract_inverted_index.such | 39 |
| abstract_inverted_index.test | 248 |
| abstract_inverted_index.that | 9, 42 |
| abstract_inverted_index.this | 64, 134 |
| abstract_inverted_index.user | 59, 103 |
| abstract_inverted_index.ways | 33 |
| abstract_inverted_index.what | 110 |
| abstract_inverted_index.0.64% | 264 |
| abstract_inverted_index.1.26% | 270 |
| abstract_inverted_index.CDCTR | 29 |
| abstract_inverted_index.Later | 195 |
| abstract_inverted_index.These | 117 |
| abstract_inverted_index.being | 143 |
| abstract_inverted_index.boost | 208 |
| abstract_inverted_index.cover | 91 |
| abstract_inverted_index.faces | 74 |
| abstract_inverted_index.input | 184 |
| abstract_inverted_index.items | 95 |
| abstract_inverted_index.large | 87 |
| abstract_inverted_index.learn | 230 |
| abstract_inverted_index.model | 45 |
| abstract_inverted_index.point | 259 |
| abstract_inverted_index.super | 82 |
| abstract_inverted_index.topic | 8 |
| abstract_inverted_index.users | 93 |
| abstract_inverted_index.using | 189 |
| abstract_inverted_index.which | 164 |
| abstract_inverted_index.work, | 135 |
| abstract_inverted_index.works | 30 |
| abstract_inverted_index.CDAnet | 154, 228, 242 |
| abstract_inverted_index.Taobao | 253 |
| abstract_inverted_index.across | 37 |
| abstract_inverted_index.design | 31 |
| abstract_inverted_index.domain | 19, 187, 211 |
| abstract_inverted_index.growth | 266 |
| abstract_inverted_index.latent | 172 |
| abstract_inverted_index.learns | 178 |
| abstract_inverted_index.mainly | 73 |
| abstract_inverted_index.matter | 109 |
| abstract_inverted_index.method | 72 |
| abstract_inverted_index.online | 246 |
| abstract_inverted_index.public | 219 |
| abstract_inverted_index.recent | 52 |
| abstract_inverted_index.source | 88 |
| abstract_inverted_index.static | 107 |
| abstract_inverted_index.target | 25, 48, 67, 97, 115, 186, 210 |
| abstract_inverted_index.(CDCTR) | 2 |
| abstract_inverted_index.Through | 214 |
| abstract_inverted_index.between | 150 |
| abstract_inverted_index.context | 112 |
| abstract_inverted_index.develop | 122 |
| abstract_inverted_index.domain, | 83, 89, 98 |
| abstract_inverted_index.domain. | 26, 49, 68, 116 |
| abstract_inverted_index.domains | 38, 176 |
| abstract_inverted_index.employs | 199 |
| abstract_inverted_index.extract | 58 |
| abstract_inverted_index.feature | 193 |
| abstract_inverted_index.improve | 236 |
| abstract_inverted_index.issues: | 76 |
| abstract_inverted_index.largely | 235 |
| abstract_inverted_index.network | 141, 159, 163, 170, 198 |
| abstract_inverted_index.perform | 146 |
| abstract_inverted_index.propose | 54, 137 |
| abstract_inverted_index.related | 18 |
| abstract_inverted_index.studies | 10 |
| abstract_inverted_index.trained | 166 |
| abstract_inverted_index.usually | 79 |
| abstract_inverted_index.(CDAnet) | 142 |
| abstract_inverted_index.However, | 69 |
| abstract_inverted_index.absolute | 257 |
| abstract_inverted_index.bringing | 255 |
| abstract_inverted_index.computes | 171 |
| abstract_inverted_index.contains | 155 |
| abstract_inverted_index.dataset, | 225 |
| abstract_inverted_index.designed | 157, 191 |
| abstract_inverted_index.domains. | 152 |
| abstract_inverted_index.existing | 28 |
| abstract_inverted_index.explicit | 55, 147, 201 |
| abstract_inverted_index.features | 173, 233 |
| abstract_inverted_index.flexible | 125 |
| abstract_inverted_index.implicit | 32 |
| abstract_inverted_index.interest | 60, 104 |
| abstract_inverted_index.leverage | 13 |
| abstract_inverted_index.motivate | 119 |
| abstract_inverted_index.proposed | 71 |
| abstract_inverted_index.relative | 263, 269 |
| abstract_inverted_index.requires | 80 |
| abstract_inverted_index.research | 7 |
| abstract_inverted_index.training | 46 |
| abstract_inverted_index.transfer | 35, 63, 131, 149 |
| abstract_inverted_index.augmented | 205 |
| abstract_inverted_index.conducted | 245 |
| abstract_inverted_index.efficient | 127 |
| abstract_inverted_index.extensive | 215 |
| abstract_inverted_index.extracted | 102 |
| abstract_inverted_index.extremely | 86 |
| abstract_inverted_index.important | 6 |
| abstract_inverted_index.increase. | 272 |
| abstract_inverted_index.knowledge | 36, 61, 65, 105, 148, 181, 203 |
| abstract_inverted_index.retrieval | 251 |
| abstract_inverted_index.technique | 128 |
| abstract_inverted_index.benchmarks | 220 |
| abstract_inverted_index.explicitly | 130 |
| abstract_inverted_index.industrial | 223 |
| abstract_inverted_index.knowledge. | 132 |
| abstract_inverted_index.meaningful | 14, 179, 231 |
| abstract_inverted_index.prediction | 3, 23 |
| abstract_inverted_index.production | 224 |
| abstract_inverted_index.techniques | 56 |
| abstract_inverted_index.translated | 232 |
| abstract_inverted_index.experiments | 216 |
| abstract_inverted_index.information | 206 |
| abstract_inverted_index.limitations | 118 |
| abstract_inverted_index.performance | 238 |
| abstract_inverted_index.prediction. | 213, 241 |
| abstract_inverted_index.regularizes | 43 |
| abstract_inverted_index.researchers | 53 |
| abstract_inverted_index.translation | 158, 169 |
| abstract_inverted_index.translator. | 194 |
| abstract_inverted_index.Cross-domain | 0 |
| abstract_inverted_index.augmentation | 140, 162, 197 |
| abstract_inverted_index.cross-domain | 139, 180, 202 |
| abstract_inverted_index.effectively, | 51 |
| abstract_inverted_index.improvement, | 261 |
| abstract_inverted_index.Specifically, | 153 |
| abstract_inverted_index.image2product | 250 |
| abstract_inverted_index.sequentially. | 167 |
| abstract_inverted_index.cross-supervised | 192 |
| abstract_inverted_index.parameter-sharing | 41 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.9257747 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |