Model Overview

Search-based Interest Model (SIM) is a system for predicting user behavior given sequences of previous interactions.

Model Architecture

SIM model consists of two components: General Search Unit (GSU) and the Exact Search Unit (ESU). The goal of the former is to filter down possibly long historical user behavior sequence to a shorter and relevant sequence. On the other hand, ESU utilizes the most recent user behaviors for a candidate item, for example, estimate click-through rate for a candidate ad. Both parts are trained jointly using data on past user behaviors.

A model architecture diagram is presented below.

\n \n
\nFigure 1. The architecture of the model.\n

Embeddings in model architecture diagram are obtained by passing each feature from the dataset through the Embedding Layer. Item features from target item, short behavior history and long behavior history share embedding tables.

\n \n
\nFigure 2. Embedding of input features.\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:

Amazon Books 2014 - Amazon Books is a category-wise subset of Amazon Product Data. It contains books reviews and metadata from Amazon, including 22.5 million reviews spanning from May 1996 to July 2014.

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 model.\n

\n\nEmbeddings in model architecture diagram are obtained by passing each feature from the dataset through the Embedding Layer. Item features from target item, short behavior history and long behavior history share embedding tables.\n\n

\n \n
\nFigure 2. Embedding of input features.\n

\n\n\n \n## Training\n\nThis model was trained using script available on [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/sim_tf2) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/SIM).\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [Amazon Books 2014](http://snap.stanford.edu/data/amazon/productGraph/) - Amazon Books is a category-wise subset of Amazon Product Data. It contains books reviews and metadata from Amazon, including 22.5 million reviews spanning from May 1996 to July 2014.\n\n\n## Performance\n\nPerformance numbers for this model are available in [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/sim_tf2/performance).\n\n## References\n- [Original paper](https://arxiv.org/abs/2006.05639)\n- [NVIDIA model implementation in NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/sim_tf2)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/SIM)\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/SIM) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","SIM checkpoint (TensorFlow2, prebatch4096)",["D",1680637826913],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/sim__tf2_ckpt_ds-amazonbooks","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[255],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_223":256,"_257":258,"_259":260,"_261":262},"Recommendation","value","uscs_recommedation","filter",{"_263":264,"_257":258},"category","label","field","application",{"_119":1440,"_121":1441},"ngcApps",[1393],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1374":1375,"_1376":1377,"_1378":1379,"_1380":1381,"_1382":276,"_151":300,"_1383":1384,"_1385":56,"_1386":98,"_1387":98,"_1388":1389,"_1390":98,"_1391":1392},"amplitude",{"_1371":1372,"_972":-7,"_1373":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1365":1366,"_1367":1368,"_1369":1370},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1247":180,"_1248":1249,"_1250":1251,"_1252":1253},"announcements",[],"consentState",{"_338":1237,"_1238":98,"_1239":31,"_1240":31,"_1241":1242},"consentConfig",{"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339},"showContactAdmin",1784153663746,[],[],[314,315,316,317,318,319],{"_45":59,"_47":70,"_49":114,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":114,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":66,"_49":114,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":114,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":114,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":114,"_51":-5,"_53":-5,"_55":56},"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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Name	Size	Updated	Actions
checkpoint	166 B	February 28, 2025 UTC	curl wget Download
ckpt-3.data-00000-of-00001	243.79 MB	February 28, 2025 UTC	curl wget Download
ckpt-3.index	8.48 KB	February 28, 2025 UTC	curl wget Download