Deep Position-wise Interaction Network for CTR Prediction Article Swipe
Jianqiang Huang
,
Ke Hu
,
Qingtao Tang
,
Mingjian Chen
,
Yi Qi
,
Jia Cheng
,
Jun Lei
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3404835.3463117
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3404835.3463117
Click-through rate (CTR) prediction plays an important role in online\nadvertising and recommender systems. In practice, the training of CTR models\ndepends on click data which is intrinsically biased towards higher positions\nsince higher position has higher CTR by nature. Existing methods such as actual\nposition training with fixed position inference and inverse propensity weighted\ntraining with no position inference alleviate the bias problem to some extend.\nHowever, the different treatment of position information between training and\ninference will inevitably lead to inconsistency and sub-optimal online\nperformance. Meanwhile, the basic assumption of these methods, i.e., the click\nprobability is the product of examination probability and relevance\nprobability, is oversimplified and insufficient to model the rich interaction\nbetween position and other information. In this paper, we propose a Deep\nPosition-wise Interaction Network (DPIN) to efficiently combine all candidate\nitems and positions for estimating CTR at each position, achieving consistency\nbetween offline and online as well as modeling the deep non-linear interaction\namong position, user, context and item under the limit of serving performance.\nFollowing our new treatment to the position bias in CTR prediction, we propose\na new evaluation metrics named PAUC (position-wise AUC) that is suitable for\nmeasuring the ranking quality at a given position. Through extensive\nexperiments on a real world dataset, we show empirically that our method is\nboth effective and efficient in solving position bias problem. We have also\ndeployed our method in production and observed statistically significant\nimprovement over a highly optimized baseline in a rigorous A/B test.\n
Related Topics
Concepts
Position (finance)
Computer science
Context (archaeology)
Inference
Consistency (knowledge bases)
Click-through rate
Artificial intelligence
Machine learning
Ranking (information retrieval)
Position paper
Deep learning
Data mining
Information retrieval
World Wide Web
Finance
Paleontology
Biology
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3404835.3463117
- OA Status
- green
- Cited By
- 20
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3155455841
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3155455841Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/3404835.3463117Digital Object Identifier
- Title
-
Deep Position-wise Interaction Network for CTR PredictionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-11Full publication date if available
- Authors
-
Jianqiang Huang, Ke Hu, Qingtao Tang, Mingjian Chen, Yi Qi, Jia Cheng, Jun LeiList of authors in order
- Landing page
-
https://doi.org/10.1145/3404835.3463117Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2106.05482Direct OA link when available
- Concepts
-
Position (finance), Computer science, Context (archaeology), Inference, Consistency (knowledge bases), Click-through rate, Artificial intelligence, Machine learning, Ranking (information retrieval), Position paper, Deep learning, Data mining, Information retrieval, World Wide Web, Finance, Paleontology, Biology, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
20Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 5, 2023: 4, 2022: 8Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2905569957, https://openalex.org/W2797400361, https://openalex.org/W2963189767, https://openalex.org/W2475334473, https://openalex.org/W2972358762, https://openalex.org/W3080768030, https://openalex.org/W2912255075, https://openalex.org/W3035716173, https://openalex.org/W2507134384, https://openalex.org/W2610314927, https://openalex.org/W3035404611, https://openalex.org/W3012600133, https://openalex.org/W2964182926, https://openalex.org/W2340526403, https://openalex.org/W2769473018, https://openalex.org/W3088393583, https://openalex.org/W2973172293, https://openalex.org/W6602120790, https://openalex.org/W2723293840, https://openalex.org/W3102540985, https://openalex.org/W2797359146, https://openalex.org/W2890044493, https://openalex.org/W3101148092, https://openalex.org/W2945944361, https://openalex.org/W3092103025, https://openalex.org/W3105712174, https://openalex.org/W2626778328, https://openalex.org/W2951001079 |
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| abstract_inverted_index.In | 13, 110 |
| abstract_inverted_index.We | 209 |
| abstract_inverted_index.an | 5 |
| abstract_inverted_index.as | 40, 138, 140 |
| abstract_inverted_index.at | 130, 183 |
| abstract_inverted_index.by | 35 |
| abstract_inverted_index.in | 8, 164, 204, 214, 225 |
| abstract_inverted_index.is | 24, 89, 97, 177 |
| abstract_inverted_index.no | 52 |
| abstract_inverted_index.of | 17, 65, 83, 92, 154 |
| abstract_inverted_index.on | 20, 189 |
| abstract_inverted_index.to | 59, 74, 101, 120, 160 |
| abstract_inverted_index.we | 113, 167, 194 |
| abstract_inverted_index.A/B | 228 |
| abstract_inverted_index.CTR | 18, 34, 129, 165 |
| abstract_inverted_index.all | 123 |
| abstract_inverted_index.and | 10, 47, 76, 95, 99, 107, 125, 136, 149, 202, 216 |
| abstract_inverted_index.for | 127 |
| abstract_inverted_index.has | 32 |
| abstract_inverted_index.new | 158, 169 |
| abstract_inverted_index.our | 157, 198, 212 |
| abstract_inverted_index.the | 15, 56, 62, 80, 87, 90, 103, 142, 152, 161, 180 |
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| abstract_inverted_index.PAUC | 173 |
| abstract_inverted_index.bias | 57, 163, 207 |
| abstract_inverted_index.data | 22 |
| abstract_inverted_index.deep | 143 |
| abstract_inverted_index.each | 131 |
| abstract_inverted_index.have | 210 |
| abstract_inverted_index.item | 150 |
| abstract_inverted_index.lead | 73 |
| abstract_inverted_index.over | 220 |
| abstract_inverted_index.rate | 1 |
| abstract_inverted_index.real | 191 |
| abstract_inverted_index.rich | 104 |
| abstract_inverted_index.role | 7 |
| abstract_inverted_index.show | 195 |
| abstract_inverted_index.some | 60 |
| abstract_inverted_index.such | 39 |
| abstract_inverted_index.that | 176, 197 |
| abstract_inverted_index.this | 111 |
| abstract_inverted_index.well | 139 |
| abstract_inverted_index.will | 71 |
| abstract_inverted_index.with | 43, 51 |
| abstract_inverted_index.(CTR) | 2 |
| abstract_inverted_index.basic | 81 |
| abstract_inverted_index.click | 21 |
| abstract_inverted_index.fixed | 44 |
| abstract_inverted_index.given | 185 |
| abstract_inverted_index.i.e., | 86 |
| abstract_inverted_index.limit | 153 |
| abstract_inverted_index.model | 102 |
| abstract_inverted_index.named | 172 |
| abstract_inverted_index.other | 108 |
| abstract_inverted_index.plays | 4 |
| abstract_inverted_index.these | 84 |
| abstract_inverted_index.under | 151 |
| abstract_inverted_index.user, | 147 |
| abstract_inverted_index.which | 23 |
| abstract_inverted_index.world | 192 |
| abstract_inverted_index.(DPIN) | 119 |
| abstract_inverted_index.biased | 26 |
| abstract_inverted_index.higher | 28, 30, 33 |
| abstract_inverted_index.highly | 222 |
| abstract_inverted_index.method | 199, 213 |
| abstract_inverted_index.online | 137 |
| abstract_inverted_index.paper, | 112 |
| abstract_inverted_index.Network | 118 |
| abstract_inverted_index.Through | 187 |
| abstract_inverted_index.between | 68 |
| abstract_inverted_index.combine | 122 |
| abstract_inverted_index.context | 148 |
| abstract_inverted_index.inverse | 48 |
| abstract_inverted_index.methods | 38 |
| abstract_inverted_index.metrics | 171 |
| abstract_inverted_index.nature. | 36 |
| abstract_inverted_index.offline | 135 |
| abstract_inverted_index.problem | 58 |
| abstract_inverted_index.product | 91 |
| abstract_inverted_index.propose | 114 |
| abstract_inverted_index.quality | 182 |
| abstract_inverted_index.ranking | 181 |
| abstract_inverted_index.serving | 155 |
| abstract_inverted_index.solving | 205 |
| abstract_inverted_index.test.\n | 229 |
| abstract_inverted_index.towards | 27 |
| abstract_inverted_index.Existing | 37 |
| abstract_inverted_index.baseline | 224 |
| abstract_inverted_index.dataset, | 193 |
| abstract_inverted_index.is\nboth | 200 |
| abstract_inverted_index.methods, | 85 |
| abstract_inverted_index.modeling | 141 |
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| abstract_inverted_index.rigorous | 227 |
| abstract_inverted_index.suitable | 178 |
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| abstract_inverted_index.alleviate | 55 |
| abstract_inverted_index.different | 63 |
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| abstract_inverted_index.efficient | 203 |
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| abstract_inverted_index.inference | 46, 54 |
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| abstract_inverted_index.insufficient | 100 |
| abstract_inverted_index.Click-through | 0 |
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| abstract_inverted_index.and\ninference | 70 |
| abstract_inverted_index.for\nmeasuring | 179 |
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