CCPIN: Classification and Combine Parallel Interaction Network for CTR Prediction
The study of feature interactions in deep neural network-based recommender systems has been a popular research area in industry and academic circles. However, the vast majority of parallel CTR prediction models do not classify the input features but instead feed them into the model. This way not onl...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/7093457 |
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| author | Guosheng Tan Changchun Yang Jiaming Jiang |
| author_facet | Guosheng Tan Changchun Yang Jiaming Jiang |
| author_sort | Guosheng Tan |
| collection | DOAJ |
| description | The study of feature interactions in deep neural network-based recommender systems has been a popular research area in industry and academic circles. However, the vast majority of parallel CTR prediction models do not classify the input features but instead feed them into the model. This way not only reduces the accuracy of the model but also ignores the effectiveness of learning individual feature interactions. In addition, the majority of parallel CTR prediction models only focus on the submodel intersections of their parallel models, ignoring the importance of the external intersection. To address the shortcomings, this paper proposes the CCPIN model on the basis of the XdeepFM model. In the CCPIN model, it can not only learn different category feature interactions but also learn individual feature interactions. Through the classification gate, adaptive features are maximized to improve the performance of the submodel. Through the Combine layer, the interaction of submodel results can be learned while retaining the original output. Through comparison experiments with other models on two datasets, it is demonstrated that the CCPIN model has an average increase of 0.93% in AUC and a decrease of 0.47% in Logloss compared to other models. |
| format | Article |
| id | doaj-art-fe3d2a0f5a05472e88486eaa4d2156f2 |
| institution | OA Journals |
| issn | 2090-0155 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-fe3d2a0f5a05472e88486eaa4d2156f22025-08-20T02:22:32ZengWileyJournal of Electrical and Computer Engineering2090-01552022-01-01202210.1155/2022/7093457CCPIN: Classification and Combine Parallel Interaction Network for CTR PredictionGuosheng Tan0Changchun Yang1Jiaming Jiang2School of Computer Science and Artificial IntelligenceSchool of Computer Science and Artificial IntelligenceSchool of Computer Science and Artificial IntelligenceThe study of feature interactions in deep neural network-based recommender systems has been a popular research area in industry and academic circles. However, the vast majority of parallel CTR prediction models do not classify the input features but instead feed them into the model. This way not only reduces the accuracy of the model but also ignores the effectiveness of learning individual feature interactions. In addition, the majority of parallel CTR prediction models only focus on the submodel intersections of their parallel models, ignoring the importance of the external intersection. To address the shortcomings, this paper proposes the CCPIN model on the basis of the XdeepFM model. In the CCPIN model, it can not only learn different category feature interactions but also learn individual feature interactions. Through the classification gate, adaptive features are maximized to improve the performance of the submodel. Through the Combine layer, the interaction of submodel results can be learned while retaining the original output. Through comparison experiments with other models on two datasets, it is demonstrated that the CCPIN model has an average increase of 0.93% in AUC and a decrease of 0.47% in Logloss compared to other models.http://dx.doi.org/10.1155/2022/7093457 |
| spellingShingle | Guosheng Tan Changchun Yang Jiaming Jiang CCPIN: Classification and Combine Parallel Interaction Network for CTR Prediction Journal of Electrical and Computer Engineering |
| title | CCPIN: Classification and Combine Parallel Interaction Network for CTR Prediction |
| title_full | CCPIN: Classification and Combine Parallel Interaction Network for CTR Prediction |
| title_fullStr | CCPIN: Classification and Combine Parallel Interaction Network for CTR Prediction |
| title_full_unstemmed | CCPIN: Classification and Combine Parallel Interaction Network for CTR Prediction |
| title_short | CCPIN: Classification and Combine Parallel Interaction Network for CTR Prediction |
| title_sort | ccpin classification and combine parallel interaction network for ctr prediction |
| url | http://dx.doi.org/10.1155/2022/7093457 |
| work_keys_str_mv | AT guoshengtan ccpinclassificationandcombineparallelinteractionnetworkforctrprediction AT changchunyang ccpinclassificationandcombineparallelinteractionnetworkforctrprediction AT jiamingjiang ccpinclassificationandcombineparallelinteractionnetworkforctrprediction |