{ "id": 305998, "title": "πŸ’₯ πŸ’₯ Deep Learning Research Papers in Product Matching πŸ’₯ πŸ’₯ ", "url": "/competitions/h-and-m-personalized-fashion-recommendations/discussion/305998", "author_name": "", "post_date": "2022-02-07T19:47:51.552004Z", "votes": 116, "comment_count": 17, "views": 0, "content": "

Google AI blog on On-device Supermarket Product Recognition
\nhttps://ai.googleblog.com/2020/07/on-device-supermarket-product.html
\nDeveloping the Key Attributes for Product Matching Based on the Item’s Image Tag Comparison
\nhttp://ceur-ws.org/Vol-2631/paper18.pdf
\nEnd-to-End Deep Kronecker-Product Matching for Person Re-identification
\nhttps://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_End-to-End_Deep_Kronecker-Product_CVPR_2018_paper.pdf
\nDeep Learning for Retail Product Recognition: Challenges and Techniques
\nhttps://www.hindawi.com/journals/cin/2020/8875910/
\nA deep learning pipeline for product recognition on store shelves
\nhttps://arxiv.org/pdf/1810.01733.pdf
\nA Flexible Large-Scale Similar Product Identification System in E-commerce
\nhttps://irsworkshop.github.io/2020/publications/paper_5_Zuo_SPIS.pdf
\nPMap: Ensemble Pre-training Models for Product Matching
\nhttps://ir-ischool-uos.github.io/mwpd/MWPD20/paper2.pdf
\nNeural Network based Extreme Classification and Similarity Models for Product Matching
\nhttps://www.aclweb.org/anthology/N18-3002.pdf
\nDeep Entity Matching with Pre-Trained Language Models
\nhttps://arxiv.org/pdf/2004.00584.pdf
\nLarge Scale Product Categorization using Structured and Unstructured Attributes
\nDeep Recurrent Neural Networks for Product Attribute Extraction in eCommerce - this one could be off the beaten path a bit, but someone could fake a title as a trick vs. a quality title that is legitimate.
\nA Clustering-Based Combinatorial Approach to Unsupervised Matching of Product Titles

\n
#
\n

Additional Resources
\nClothes-Matching-Based-on-Machine-Learning-Algorithms
\nhttps://github.com/Sapphirine/Clothes-Matching-Based-on-Machine-Learning-Algorithms
\nSupervised-Product-Similarity
\nhttps://github.com/BinaryWiz/Supervised-Product-Similarity
\nProduct-Matching-using-Deep-Learning using GANs
\nhttps://github.com/gjain307/Product-Matching-using-Deep-Learning
\nProduct-matching-model
\nhttps://github.com/jahyeha/product-matching-model
\nProduct categorization and named entity recognition
\nhttps://github.com/etano/productner
\nUnsupervised Product Matching Using Combinations and Permutations
\nhttps://github.com/BinaryWiz/UPM
\nEntity Matching for Online Marketplaces
\nhttps://github.com/kylegilde/Entity-Matching-in-Online-Retail
\nDitto: Deep Entity Matching with Pre-Trained Language Models
\nhttps://github.com/megagonlabs/ditto

", "messages": [ { "id": "1680433", "postDate": "02/07/2022 19:47:51", "content": "

Google AI blog on On-device Supermarket Product Recognition
\nhttps://ai.googleblog.com/2020/07/on-device-supermarket-product.html
\nDeveloping the Key Attributes for Product Matching Based on the Item’s Image Tag Comparison
\nhttp://ceur-ws.org/Vol-2631/paper18.pdf
\nEnd-to-End Deep Kronecker-Product Matching for Person Re-identification
\nhttps://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_End-to-End_Deep_Kronecker-Product_CVPR_2018_paper.pdf
\nDeep Learning for Retail Product Recognition: Challenges and Techniques
\nhttps://www.hindawi.com/journals/cin/2020/8875910/
\nA deep learning pipeline for product recognition on store shelves
\nhttps://arxiv.org/pdf/1810.01733.pdf
\nA Flexible Large-Scale Similar Product Identification System in E-commerce
\nhttps://irsworkshop.github.io/2020/publications/paper_5_Zuo_SPIS.pdf
\nPMap: Ensemble Pre-training Models for Product Matching
\nhttps://ir-ischool-uos.github.io/mwpd/MWPD20/paper2.pdf
\nNeural Network based Extreme Classification and Similarity Models for Product Matching
\nhttps://www.aclweb.org/anthology/N18-3002.pdf
\nDeep Entity Matching with Pre-Trained Language Models
\nhttps://arxiv.org/pdf/2004.00584.pdf
\nLarge Scale Product Categorization using Structured and Unstructured Attributes
\nDeep Recurrent Neural Networks for Product Attribute Extraction in eCommerce - this one could be off the beaten path a bit, but someone could fake a title as a trick vs. a quality title that is legitimate.
\nA Clustering-Based Combinatorial Approach to Unsupervised Matching of Product Titles

\n
#
\n

Additional Resources
\nClothes-Matching-Based-on-Machine-Learning-Algorithms
\nhttps://github.com/Sapphirine/Clothes-Matching-Based-on-Machine-Learning-Algorithms
\nSupervised-Product-Similarity
\nhttps://github.com/BinaryWiz/Supervised-Product-Similarity
\nProduct-Matching-using-Deep-Learning using GANs
\nhttps://github.com/gjain307/Product-Matching-using-Deep-Learning
\nProduct-matching-model
\nhttps://github.com/jahyeha/product-matching-model
\nProduct categorization and named entity recognition
\nhttps://github.com/etano/productner
\nUnsupervised Product Matching Using Combinations and Permutations
\nhttps://github.com/BinaryWiz/UPM
\nEntity Matching for Online Marketplaces
\nhttps://github.com/kylegilde/Entity-Matching-in-Online-Retail
\nDitto: Deep Entity Matching with Pre-Trained Language Models
\nhttps://github.com/megagonlabs/ditto

", "rawMarkdown": "**Google AI blog on On-device Supermarket Product Recognition**\n\nhttps://ai.googleblog.com/2020/07/on-device-supermarket-product.html\n\n**Developing the Key Attributes for Product Matching Based on the Item’s Image Tag Comparison**\n\nhttp://ceur-ws.org/Vol-2631/paper18.pdf\n\n**End-to-End Deep Kronecker-Product Matching for Person Re-identification**\n\nhttps://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_End-to-End_Deep_Kronecker-Product_CVPR_2018_paper.pdf\n\n**Deep Learning for Retail Product Recognition: Challenges and Techniques**\n\nhttps://www.hindawi.com/journals/cin/2020/8875910/\n\n**A deep learning pipeline for product recognition on store shelves**\n\nhttps://arxiv.org/pdf/1810.01733.pdf\n\n**A Flexible Large-Scale Similar Product Identification System in E-commerce**\n\nhttps://irsworkshop.github.io/2020/publications/paper_5_Zuo_SPIS.pdf\n\n**PMap: Ensemble Pre-training Models for Product Matching**\n\nhttps://ir-ischool-uos.github.io/mwpd/MWPD20/paper2.pdf\n\n**Neural Network based Extreme Classification and Similarity Models for Product Matching**\n\nhttps://www.aclweb.org/anthology/N18-3002.pdf\n\n**Deep Entity Matching with Pre-Trained Language Models**\nhttps://arxiv.org/pdf/2004.00584.pdf\n\n**Large Scale Product Categorization using Structured and Unstructured Attributes**\n\nDeep Recurrent Neural Networks for Product Attribute Extraction in eCommerce - this one could be off the beaten path a bit, but someone could fake a title as a trick vs. a quality title that is legitimate.\n\n**A Clustering-Based Combinatorial Approach to Unsupervised Matching of Product Titles**\n\n\n#############################################################\n\n\n**Additional Resources**\n\n\n**Clothes-Matching-Based-on-Machine-Learning-Algorithms**\n\nhttps://github.com/Sapphirine/Clothes-Matching-Based-on-Machine-Learning-Algorithms\n\n**Supervised-Product-Similarity**\n\nhttps://github.com/BinaryWiz/Supervised-Product-Similarity\n\n**Product-Matching-using-Deep-Learning using GANs**\n\nhttps://github.com/gjain307/Product-Matching-using-Deep-Learning \n\n**Product-matching-model**\n\nhttps://github.com/jahyeha/product-matching-model\n\n**Product categorization and named entity recognition**\n\nhttps://github.com/etano/productner\n\n**Unsupervised Product Matching Using Combinations and Permutations**\n\nhttps://github.com/BinaryWiz/UPM\n\n**Entity Matching for Online Marketplaces**\nhttps://github.com/kylegilde/Entity-Matching-in-Online-Retail\n\n**Ditto: Deep Entity Matching with Pre-Trained Language Models**\nhttps://github.com/megagonlabs/ditto", "votes": null }, { "id": "1682541", "postDate": "02/09/2022 08:32:14", "content": "

As always wonderful contributor thanks a ton.

", "rawMarkdown": "As always wonderful contributor thanks a ton.", "votes": null }, { "id": "1683719", "postDate": "02/10/2022 02:21:55", "content": "

Great work as always!

", "rawMarkdown": "Great work as always!", "votes": null }, { "id": "1684691", "postDate": "02/10/2022 16:48:04", "content": "

Thank you for sharing!

", "rawMarkdown": "Thank you for sharing!", "votes": null }, { "id": "1684963", "postDate": "02/10/2022 22:23:49", "content": "

Thank you for sharing! @usharengaraju

", "rawMarkdown": "Thank you for sharing! @usharengaraju", "votes": null }, { "id": "1685803", "postDate": "02/11/2022 14:54:18", "content": "

Thank you for sharing!

", "rawMarkdown": "Thank you for sharing!", "votes": null }, { "id": "1686207", "postDate": "02/11/2022 21:07:54", "content": "

Thank you for sharing

", "rawMarkdown": "Thank you for sharing", "votes": null }, { "id": "1686381", "postDate": "02/12/2022 02:44:57", "content": "

You are the best.

", "rawMarkdown": "You are the best.", "votes": null }, { "id": "1687376", "postDate": "02/12/2022 19:35:38", "content": "

I would like to add a few more :

\n

How Amazon, Walmart, and Pinterest use computer vision.

\n

https://www.amazon.science/blog/how-computer-vision-will-help-amazon-customers-shop-online

\n

https://medium.com/walmartglobaltech/product-matching-in-ecommerce-4f19b6aebaca

\n

https://towardsdatascience.com/pinterests-visual-lens-how-computer-vision-explores-your-taste-5470f87502ad

", "rawMarkdown": "I would like to add a few more :\n\nHow Amazon, Walmart, and Pinterest use computer vision.\n\n\nhttps://www.amazon.science/blog/how-computer-vision-will-help-amazon-customers-shop-online\n\nhttps://medium.com/walmartglobaltech/product-matching-in-ecommerce-4f19b6aebaca\n\nhttps://towardsdatascience.com/pinterests-visual-lens-how-computer-vision-explores-your-taste-5470f87502ad", "votes": null }, { "id": "1687551", "postDate": "02/12/2022 23:50:40", "content": "

Thank you for putting this collection together.

", "rawMarkdown": "Thank you for putting this collection together.", "votes": null }, { "id": "1688603", "postDate": "02/13/2022 18:11:18", "content": "

Amazing collection & Thanks for sharing @usharengaraju πŸ‘

", "rawMarkdown": "Amazing collection & Thanks for sharing @usharengaraju πŸ‘", "votes": null }, { "id": "1693265", "postDate": "02/16/2022 14:58:18", "content": "

Thank you for sharing!

", "rawMarkdown": "Thank you for sharing!", "votes": null }, { "id": "1694712", "postDate": "02/17/2022 16:52:17", "content": "

Thank you for sharing!

", "rawMarkdown": "Thank you for sharing!", "votes": null }, { "id": "1700222", "postDate": "02/21/2022 18:45:26", "content": "

Thanks a lot, great work as always πŸ‘πŸ»

", "rawMarkdown": "Thanks a lot, great work as always πŸ‘πŸ»", "votes": null }, { "id": "1703290", "postDate": "02/24/2022 11:04:30", "content": "

Thank you very much!

", "rawMarkdown": "Thank you very much!", "votes": null }, { "id": "1763352", "postDate": "04/21/2022 13:04:15", "content": "

thank you so much this is what i was looking for

", "rawMarkdown": "thank you so much this is what i was looking for", "votes": null }, { "id": "1764604", "postDate": "04/22/2022 16:16:43", "content": "

Very useful collection of research papers, Thank You

", "rawMarkdown": "Very useful collection of research papers, Thank You", "votes": null }, { "id": "1826652", "postDate": "06/20/2022 14:14:11", "content": "

Thank you for sharing!!

", "rawMarkdown": "Thank you for sharing!!", "votes": null } ], "comments": [ { "id": 1682541, "author_name": "gazu468", "author_url": "", "post_date": "02/09/2022 08:32:14", "content": "

As always wonderful contributor thanks a ton.

", "votes": null, "replies": [] }, { "id": 1683719, "author_name": "crained", "author_url": "", "post_date": "02/10/2022 02:21:55", "content": "

Great work as always!

", "votes": null, "replies": [] }, { "id": 1684691, "author_name": "rudiswtn", "author_url": "", "post_date": "02/10/2022 16:48:04", "content": "

Thank you for sharing!

", "votes": null, "replies": [] }, { "id": 1684963, "author_name": "nebipeker", "author_url": "", "post_date": "02/10/2022 22:23:49", "content": "

Thank you for sharing! @usharengaraju

", "votes": null, "replies": [] }, { "id": 1685803, "author_name": "abdulmanankhalid", "author_url": "", "post_date": "02/11/2022 14:54:18", "content": "

Thank you for sharing!

", "votes": null, "replies": [] }, { "id": 1686207, "author_name": "wmaximilian", "author_url": "", "post_date": "02/11/2022 21:07:54", "content": "

Thank you for sharing

", "votes": null, "replies": [] }, { "id": 1686381, "author_name": "stephenadjignon", "author_url": "", "post_date": "02/12/2022 02:44:57", "content": "

You are the best.

", "votes": null, "replies": [] }, { "id": 1687376, "author_name": "chiragtagadiya", "author_url": "", "post_date": "02/12/2022 19:35:38", "content": "

I would like to add a few more :

\n

How Amazon, Walmart, and Pinterest use computer vision.

\n

https://www.amazon.science/blog/how-computer-vision-will-help-amazon-customers-shop-online

\n

https://medium.com/walmartglobaltech/product-matching-in-ecommerce-4f19b6aebaca

\n

https://towardsdatascience.com/pinterests-visual-lens-how-computer-vision-explores-your-taste-5470f87502ad

", "votes": null, "replies": [] }, { "id": 1687551, "author_name": "lukeflaherty", "author_url": "", "post_date": "02/12/2022 23:50:40", "content": "

Thank you for putting this collection together.

", "votes": null, "replies": [] }, { "id": 1688603, "author_name": "aishwarya2210", "author_url": "", "post_date": "02/13/2022 18:11:18", "content": "

Amazing collection & Thanks for sharing @usharengaraju πŸ‘

", "votes": null, "replies": [] }, { "id": 1693265, "author_name": "koyayamamoto", "author_url": "", "post_date": "02/16/2022 14:58:18", "content": "

Thank you for sharing!

", "votes": null, "replies": [] }, { "id": 1694712, "author_name": "viji81", "author_url": "", "post_date": "02/17/2022 16:52:17", "content": "

Thank you for sharing!

", "votes": null, "replies": [] }, { "id": 1700222, "author_name": "datascientistfp", "author_url": "", "post_date": "02/21/2022 18:45:26", "content": "

Thanks a lot, great work as always πŸ‘πŸ»

", "votes": null, "replies": [] }, { "id": 1703290, "author_name": "lorenzoagnolucci", "author_url": "", "post_date": "02/24/2022 11:04:30", "content": "

Thank you very much!

", "votes": null, "replies": [] }, { "id": 1763352, "author_name": "parkseongmin", "author_url": "", "post_date": "04/21/2022 13:04:15", "content": "

thank you so much this is what i was looking for

", "votes": null, "replies": [] }, { "id": 1764604, "author_name": "hisudha", "author_url": "", "post_date": "04/22/2022 16:16:43", "content": "

Very useful collection of research papers, Thank You

", "votes": null, "replies": [] }, { "id": 1826652, "author_name": "yuetchxuw", "author_url": "", "post_date": "06/20/2022 14:14:11", "content": "

Thank you for sharing!!

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1680433": "**Google AI blog on On-device Supermarket Product Recognition**\n\nhttps://ai.googleblog.com/2020/07/on-device-supermarket-product.html\n\n**Developing the Key Attributes for Product Matching Based on the Item’s Image Tag Comparison**\n\nhttp://ceur-ws.org/Vol-2631/paper18.pdf\n\n**End-to-End Deep Kronecker-Product Matching for Person Re-identification**\n\nhttps://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_End-to-End_Deep_Kronecker-Product_CVPR_2018_paper.pdf\n\n**Deep Learning for Retail Product Recognition: Challenges and Techniques**\n\nhttps://www.hindawi.com/journals/cin/2020/8875910/\n\n**A deep learning pipeline for product recognition on store shelves**\n\nhttps://arxiv.org/pdf/1810.01733.pdf\n\n**A Flexible Large-Scale Similar Product Identification System in E-commerce**\n\nhttps://irsworkshop.github.io/2020/publications/paper_5_Zuo_SPIS.pdf\n\n**PMap: Ensemble Pre-training Models for Product Matching**\n\nhttps://ir-ischool-uos.github.io/mwpd/MWPD20/paper2.pdf\n\n**Neural Network based Extreme Classification and Similarity Models for Product Matching**\n\nhttps://www.aclweb.org/anthology/N18-3002.pdf\n\n**Deep Entity Matching with Pre-Trained Language Models**\nhttps://arxiv.org/pdf/2004.00584.pdf\n\n**Large Scale Product Categorization using Structured and Unstructured Attributes**\n\nDeep Recurrent Neural Networks for Product Attribute Extraction in eCommerce - this one could be off the beaten path a bit, but someone could fake a title as a trick vs. a quality title that is legitimate.\n\n**A Clustering-Based Combinatorial Approach to Unsupervised Matching of Product Titles**\n\n\n#############################################################\n\n\n**Additional Resources**\n\n\n**Clothes-Matching-Based-on-Machine-Learning-Algorithms**\n\nhttps://github.com/Sapphirine/Clothes-Matching-Based-on-Machine-Learning-Algorithms\n\n**Supervised-Product-Similarity**\n\nhttps://github.com/BinaryWiz/Supervised-Product-Similarity\n\n**Product-Matching-using-Deep-Learning using GANs**\n\nhttps://github.com/gjain307/Product-Matching-using-Deep-Learning \n\n**Product-matching-model**\n\nhttps://github.com/jahyeha/product-matching-model\n\n**Product categorization and named entity recognition**\n\nhttps://github.com/etano/productner\n\n**Unsupervised Product Matching Using Combinations and Permutations**\n\nhttps://github.com/BinaryWiz/UPM\n\n**Entity Matching for Online Marketplaces**\nhttps://github.com/kylegilde/Entity-Matching-in-Online-Retail\n\n**Ditto: Deep Entity Matching with Pre-Trained Language Models**\nhttps://github.com/megagonlabs/ditto", "1682541": "As always wonderful contributor thanks a ton.", "1683719": "Great work as always!", "1684691": "Thank you for sharing!", "1684963": "Thank you for sharing! @usharengaraju", "1685803": "Thank you for sharing!", "1686207": "Thank you for sharing", "1686381": "You are the best.", "1687376": "I would like to add a few more :\n\nHow Amazon, Walmart, and Pinterest use computer vision.\n\n\nhttps://www.amazon.science/blog/how-computer-vision-will-help-amazon-customers-shop-online\n\nhttps://medium.com/walmartglobaltech/product-matching-in-ecommerce-4f19b6aebaca\n\nhttps://towardsdatascience.com/pinterests-visual-lens-how-computer-vision-explores-your-taste-5470f87502ad", "1687551": "Thank you for putting this collection together.", "1688603": "Amazing collection & Thanks for sharing @usharengaraju πŸ‘", "1693265": "Thank you for sharing!", "1694712": "Thank you for sharing!", "1700222": "Thanks a lot, great work as always πŸ‘πŸ»", "1703290": "Thank you very much!", "1763352": "thank you so much this is what i was looking for", "1764604": "Very useful collection of research papers, Thank You", "1826652": "Thank you for sharing!!" }, "source": "meta" }