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https://github.com/eifuentes/awesome-embeddings

🪁A curated list of awesome resources around entity embeddings
https://github.com/eifuentes/awesome-embeddings

List: awesome-embeddings

awesome awesome-list deep-learning embedding embeddings feature-engineering machine-learning

Last synced: 16 days ago
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🪁A curated list of awesome resources around entity embeddings

Host: GitHub
URL: https://github.com/eifuentes/awesome-embeddings
Owner: eifuentes
License: cc0-1.0
Created: 2020-06-19T05:53:59.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2022-01-02T21:07:29.000Z (almost 3 years ago)
Last Synced: 2024-05-23T04:10:10.235Z (7 months ago)
Topics: awesome, awesome-list, deep-learning, embedding, embeddings, feature-engineering, machine-learning
Homepage:
Size: 18.6 KB
Stars: 8
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: readme.md
- Contributing: contributing.md
- License: license
- Code of conduct: code-of-conduct.md

Awesome Lists containing this project

ultimate-awesome - awesome-embeddings - 🪁A curated list of awesome resources around entity embeddings. (Other Lists / Monkey C Lists)

README

        # Awsome Embeddings

[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

> A curated list of awesome resources around entity embeddings.

## Contents

- [Recommendation](#recommendation)

    - [Articles & Papers](#articles)

    - [Tools](#tools)

    - [Datasets](#datasets)

    - [Videos](#videos)

    - [Lists](#lists)

## Recommendation

Recommendations engines based on entity embeddings.

### Articles

- [Applying Deep Learning to Related Pins](https://medium.com/the-graph/applying-deep-learning-to-related-pins-a6fee3c92f5e)

- [Applying word2vec to Recommenders and Advertising](http://mccormickml.com/2018/06/15/applying-word2vec-to-recommenders-and-advertising/)

- [Instagram’s Explore recommender system](https://ai.facebook.com/blog/powered-by-ai-instagrams-explore-recommender-system/)

- [Home Embeddings for Similar Home Recommendations](https://www.zillow.com/tech/embedding-similar-home-recommendation/)

- [Building a Recommender System Using Embeddings](https://drop.engineering/building-a-recommender-system-using-embeddings-de5a30e655aa)

- [Deep neural networks for YouTube recommendations](https://blog.acolyer.org/2016/09/19/deep-neural-networks-for-youtube-recommendations/)

- [The Illustrated Word2vec](https://jalammar.github.io/illustrated-word2vec/)

- [Real-time Personalization using Embeddings for Search Ranking at Airbnb](https://www.kdd.org/kdd2018/accepted-papers/view/real-time-personalization-using-embeddings-for-search-ranking-at-airbnb)

- [Listing Embeddings in Search Ranking](https://medium.com/airbnb-engineering/listing-embeddings-for-similar-listing-recommendations-and-real-time-personalization-in-search-601172f7603e)

- [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://www.kdd.org/kdd2018/accepted-papers/view/billion-scale-commodity-embedding-for-e-commerce-recommendation-in-alibaba)

- [Machine learning @ Spotify](https://www.slideshare.net/AndySloane/machine-learning-spotify-madison-big-data-meetup)

- [Using Word2vec for Music Recommendations](https://towardsdatascience.com/using-word2vec-for-music-recommendations-bb9649ac2484)

- [Embedding Everything for Anything2Anything Recommendations](https://making.dia.com/embedding-everything-for-anything2anything-recommendations-fca7f58f53ff)

- [Learning Embeddings for Music Recommendation with MXNet’s Sparse API](https://medium.com/apache-mxnet/learning-embeddings-for-music-recommendation-with-mxnets-sparse-api-5698f4d7d8)

- [How GOAT Taught a Machine to Love Sneakers](https://medium.com/engineeringatgoat/how-goat-taught-a-machine-to-love-sneakers-e4a97cda71b1)

- [Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf)

- [Deep Learning with Emojis Not Math](https://tech.instacart.com/deep-learning-with-emojis-not-math-660ba1ad6cdc)

- [Collaborative Filtering for Implicit Feedback Datasets](http://yifanhu.net/PUB/cf.pdf)

- [Applications of the conjugate gradient method for implicit feedback collaborative filtering](https://www.semanticscholar.org/paper/Applications-of-the-conjugate-gradient-method-for-Takács-Pilászy/bfdf7af6cf7fd7bb5e6b6db5bbd91be11597eaf0?p2df)

- [BPR: Bayesian Personalized Ranking from Implicit Feedback](https://arxiv.org/pdf/1205.2618.pdf)

- [WSABIE: Scaling Up To Large Vocabulary Image Annotation](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37180.pdf) aka WARP

- [Logistic Matrix Factorization for Implicit Feedback Data](https://web.stanford.edu/~rezab/nips2014workshop/submits/logmat.pdf)

- [Revisiting the Performance of iALS on Item Recommendation Benchmarks](https://arxiv.org/pdf/2110.14037v1.pdf)

- [Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031v2.pdf)

- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://arxiv.org/pdf/1904.06690.pdf)

- [Self-Attentive Hawkes Process](https://arxiv.org/pdf/1907.07561.pdf)

- [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/pdf/1905.06874.pdf)

- [Variational Autoencoders for Collaborative Filtering](https://arxiv.org/pdf/1802.05814.pdf)

- [Towards Large Scale Training Of Autoencoders For Collaborative Filtering](https://arxiv.org/pdf/1809.00999.pdf)

- [Degenerate Feedback Loops in Recommender Systems](https://arxiv.org/abs/1902.10730)

- [Deconvolving Feedback Loops in Recommender Systems](https://proceedings.neurips.cc/paper/2016/file/962e56a8a0b0420d87272a682bfd1e53-Paper.pdf)

- [Collaborative Filtering with Temporal Dynamics](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.379.1951&rep=rep1&type=pdf)

- [Learning Representations of Hierarchical Slates in Collaborative Filtering](https://dl.acm.org/doi/10.1145/3383313.3418484)

- [Tuning Word2vec for Large Scale Recommendation Systems](https://dl.acm.org/doi/10.1145/3383313.3418486)

- [Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms](https://arxiv.org/abs/1911.00936v1)

- [Embarrassingly Shallow Autoencoders for Sparse Data](https://arxiv.org/pdf/1905.03375v1.pdf)

- [Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)](https://arxiv.org/abs/2102.05774v1)

- [RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback](https://arxiv.org/abs/1912.11160v1)

- [On the Difficulty of Evaluating Baselines: A Study on Recommender Systems](https://arxiv.org/abs/1905.01395v1)

- [Hybrid Recommender System based on Autoencoders](https://arxiv.org/pdf/1606.07659v3.pdf)

- [AutoRec: Autoencoders Meet Collaborative Filtering](http://users.cecs.anu.edu.au/~u5098633/papers/www15.pdf)

- [Self-Attentive Sequential Recommendation](https://arxiv.org/abs/1808.09781v1)

- [Scaling Factorization Machines to Relational Data](http://www.vldb.org/pvldb/vol6/p337-rendle.pdf)

- [Bayesian Factorization Machines](https://www.ismll.uni-hildesheim.de/pub/pdfs/FreudenthalerRendle_BayesianFactorizationMachines.pdf)

- [Challenging the Long Tail Recommendation](https://arxiv.org/abs/1205.6700)

- [Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model](https://people.engr.tamu.edu/huangrh/Spring16/papers_course/matrix_factorization.pdf)

- [Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf) aka GAUC

### Tools

- [Spotlight](https://github.com/maciejkula/spotlight)

- [LightFM](https://github.com/lyst/lightfm)

- [Implicit](https://github.com/benfred/implicit)

- [Microsoft Recommenders](https://github.com/microsoft/recommenders)

- [Tensorflow Recommenders](https://github.com/tensorflow/recommenders)

- [Facebook DLRM](https://github.com/facebookresearch/dlrm)

- [NVIDIA HugeCTR](https://github.com/NVIDIA-Merlin/HugeCTR)

- [NVIDIA Transformers4Rec](https://github.com/NVIDIA-Merlin/Transformers4Rec)

- [NVIDIA DeepRecommender](https://github.com/NVIDIA/DeepRecommender)

- [RecNN](https://github.com/awarebayes/RecNN)

- [OpenRec](https://github.com/ylongqi/openrec)

- [Flurs](https://github.com/takuti/flurs)

- [DeepMatch](https://github.com/shenweichen/DeepMatch)

- [Recoder](https://github.com/amoussawi/recoder)

### Datasets

- [Criteo 1TB Click Logs](https://ailab.criteo.com/download-criteo-1tb-click-logs-dataset/)

    - [Kaggle Version](https://www.kaggle.com/c/criteo-display-ad-challenge)

### Videos

- [How NVIDIA Supports Recommender Systems](https://www.youtube.com/watch?v=wPso35VkuCs)

- [RecSys 2020 Tutorial: Feature Engineering for Recommender Systems](https://www.youtube.com/watch?v=uROvhp7cj6Q)

- [Grandmaster Series – How to Build a Winning Deep Learning Recommender System](https://www.youtube.com/watch?v=bHuww-l_Sq0)

### Lists

- [Papers with Code - Recsys](https://paperswithcode.com/task/recommendation-systems)

## Contribute

Contributions welcome! Read the [contribution guidelines](contributing.md) first.