Model Overview

Wide & Deep Recommender model.

Model Architecture

Wide & Deep refers to a class of networks that use the output of two parts working in parallel - wide model and deep model - to make a binary prediction of CTR. The wide model is a linear model of features together with their transforms. The deep model is a series of 5 hidden MLP layers of 1024 neurons. The model can handle both numerical continuous features as well as categorical features represented as dense embeddings. The architecture of the model is presented in Figure 1.

\n \n
\nFigure 1. The architecture of the Wide & Deep model.\n

Training

This model was trained using script available on NGC and in GitHub repo

Dataset

The following datasets were used to train this model:

Outbrain - Dataset containing a sample of users’ page views and clicks, as observed on multiple publisher sites in the United States between 14-June-2016 and 28-June-2016. Each viewed page or clicked recommendation is further accompanied by some semantic attributes of those documents.

Performance

Performance numbers for this model are available in NGC

References

License

This model was trained using open-source software available in Deep Learning Examples repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.

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\n \n
\nFigure 1. The architecture of the Wide & Deep model.\n

\n\n\n \n## Training\n\nThis model was trained using script available on [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:wideanddeep_for_tensorflow2) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/WideAndDeep)\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [Outbrain](https://www.kaggle.com/c/outbrain-click-prediction/data) - Dataset containing a sample of users’ page views and clicks, as observed on multiple publisher sites in the United States between 14-June-2016 and 28-June-2016. Each viewed page or clicked recommendation is further accompanied by some semantic attributes of those documents.\n\n\n## Performance\n\nPerformance numbers for this model are available in [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:wideanddeep_for_tensorflow2/performance)\n\n## References\n- [original paper](https://arxiv.org/abs/1606.07792)\n- [NVIDIA model implementation in NGC](https://ngc.nvidia.com/catalog/resources/nvidia:wideanddeep_for_tensorflow2)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/WideAndDeep)\n\n## License\n\nThis model was trained using open-source software available in [Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/WideAndDeep) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","Wide & Deep TF2 checkpoint (Base, 128k, AMP, NVTabular)",["D",1680635401875],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/models/widedeep_tf2_amp_base_128k_nvtabular","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes",729933530,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[262,263],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_229":273,"_265":274,"_267":275,"_269":270},{"_229":264,"_265":266,"_267":268,"_269":270},"Recommendation","value","uscs_recommedation","filter",{"_271":272,"_265":266},"category","label","field","application","Deep Learning Examples","pltfm_deep_learning_examples",{"_271":276,"_265":274},"platform",{"_126":1451,"_128":1452},"ngcApps",[1404],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1386":1387,"_1388":1389,"_1390":1391,"_1392":1393,"_1394":288,"_158":312,"_1395":65,"_1396":55,"_1397":105,"_1398":105,"_1399":1400,"_1401":105,"_1402":1403},"amplitude",{"_1383":1384,"_985":-7,"_1385":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1377":1378,"_1379":1380,"_1381":1382},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1260":186,"_1261":1262,"_1263":1264,"_1265":1266},"announcements",[],"consentState",{"_350":1250,"_1251":105,"_1252":31,"_1253":31,"_1254":1255},"consentConfig",{"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351},"showContactAdmin",1784207192835,[],[],[326,327,328,329,330,331],{"_45":59,"_47":70,"_49":121,"_51":-5,"_53":-5,"_54":55},{"_45":59,"_47":68,"_49":121,"_51":-5,"_53":-5,"_54":55},{"_45":59,"_47":66,"_49":121,"_51":-5,"_53":-5,"_54":55},{"_45":59,"_47":60,"_49":121,"_51":-5,"_53":-5,"_54":55},{"_45":56,"_47":57,"_49":121,"_51":-5,"_53":-5,"_54":55},{"_45":46,"_47":48,"_49":121,"_51":-5,"_53":-5,"_54":55},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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