Resources for incorporating image embeddings, joint image/word embeddings, multi lingual word embeddings for learning to rank or as a replacement for things like BM25 or TFIDF.
- Kamelia Aryafar (Overstock, formerly Etsy) Learning to Rank in Ecommerce AIWTB 2017 https://arxiv.org/abs/1511.06746 (Nov 2015) https://www.youtube.com/watch?v=QjTi1qcLTQw
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In production retrieve top k documents using SOLR and bm25. Then rerank the the returned results. Concatenation of transfer learned image embeddings from a VGG model trained on ImageNet with bag of words Learning rank from search logs using click and ignore signals
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Great examples of overcoming user labels for Wedding dresses that are not relevant.
- Ethan Rosenthal (Dia & Co, Birchbox)
Ethan writes very accessible content and prefers methods that are intuitive.
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He obtained great results from transfer learning on ImageNet VGG model after expending significant effort trying to incorporate side channel information using matrix factorization methods ( WRMF weighted regularized matrix factorization )
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Diving into the deep end of clothing styles
https://www.youtube.com/watch?v=Pm4ZQMKoz7Q
http://blog.ethanrosenthal.com/
http://blog.ethanrosenthal.com/2017/06/20/matrix-factorization-in-pytorch/
https://github.com/EthanRosenthal -
Slide presentation links from Ethan
https://www.slideshare.net/rosentep/diving-into-the-deep-end-of-clothing-styles-pydata-nyc-2017
https://www.slideshare.net/CalvinGiles/finding-needles-in-haystacks-with-deep-neural-networks
https://www.slideshare.net/AhmadQamar3/using-deep-neural-networks-for-fashion-applications
https://medium.com/mlreview/how-to-apply-distance-metric-learning-for-street-to-shop-problem-d21247723d2a
- Ivona Tautkute
- good use case Ikea furniture
- Ikea would have structured data to use as labels for images
- What looks good with my sofa?
https://www.youtube.com/watch?v=lA9swUze2kg
https://arxiv.org/abs/1707.06907
https://github.com/ivonatau
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Han Xiao (Zalando)
Building Cross-Lingual End-to-End Product Search with Tensorflow
https://hanxiao.github.io/2018/01/10/Build-Cross-Lingual-End-to-End-Product-Search-using-Tensorflow/ -
Babylonpartners / fastText_multilingual
alignment of multiple fastText word embeddings into a single vector space
https://github.com/Babylonpartners/fastText_multilingual -
Aleksander Movchan
- He assumes that you are already are familiar with "triplet loss functions"
- Triplet loss functions use an anchor, positive example, and a negative example
- You choose anchor, negative example pairs that are more similar to speed up training
* identifying a fashion item in a user image and finding it in an online shop
https://medium.com/mlreview/how-to-apply-distance-metric-learning-for-street-to-shop-problem-d21247723d2a - Florian Schoff 2015 Facenet paper that explains how to train models that use Triplet loss functions
https://arxiv.org/abs/1503.03832
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Shutterstock
Great examples of overcoming disambiguation due to stemming.
hawk -> some 'Stephen Hawking' along with 'hawk' that is a bird.
angel -> 'Los Angeles' more than angel
https://tech.shutterstock.com/2017/03/08/image-search-using-joint-embeddings-part-one/
https://tech.shutterstock.com/2017/04/13/image-search-using-joint-embeddings-part-two/ -
Lyse clothing search blog post
https://making.lyst.com/2018/01/10/a-machine-learning-model-to-understand-fashion-search-queries/ -
Deep Fashion
http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html -
Dan Gillick (Google NLP group, instructor at Berkeley)
Student talk at Berkeley
- learning embeddings in the same vector space for more than one media type multi lingual search
- Increasing relevancy by returning more results from tail of documents.
- They mostly use Google search logs
https://www.youtube.com/watch?v=JGHVJXP9NHw
- Google AutoML announcment
Use cases that are mostly about learning product attributes https://www.blog.google/topics/google-cloud/cloud-automl-making-ai-accessible-every-business/