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Curated list of recommnedation system topics
https://github.com/creyesp/Awesome-recsys
List: Awesome-recsys
awesome-list deep-learning machine-learning papers personalization recommender-system recsys
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Curated list of recommnedation system topics
Host: GitHub
URL: https://github.com/creyesp/Awesome-recsys
Owner: creyesp
Created: 2021-06-03T12:57:06.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2024-03-20T11:09:18.000Z (9 months ago)
Last Synced: 2024-11-20T13:02:56.344Z (about 1 month ago)
Topics: awesome-list, deep-learning, machine-learning, papers, personalization, recommender-system, recsys
Homepage:
Size: 527 KB
Stars: 109
Watchers: 7
Forks: 16
Open Issues: 2
Metadata Files:
- Readme: README.md
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ultimate-awesome - Awesome-recsys - Curated list of recommnedation system topics. (Other Lists / Monkey C Lists)
README

        # Awesome-recsys

"Recommender systems are often characterized as tools that help users in their decision-making process"

1. [Blogs post](#blogs-post)

2. [Videos](#Videos)

3. [Online Courses](#Online-Courses)

4. [Books](#books)

5. [Code](#code)

6. [Datasets](#datasets)

7. [Papers](#papers)

8. [Other Awesone list](#other-awesone-list)

## Blogs post

* https://medium.datadriveninvestor.com/how-to-build-a-recommendation-system-for-purchase-data-step-by-step-d6d7a78800b6

* https://www.kaggle.com/c/santander-product-recommendation

* https://www.kaggle.com/retailrocket/ecommerce-dataset/home

* https://www.kaggle.com/dschettler8845/recsys-2020-ecommerce-dataset/tasks?taskId=3124

* https://www.kaggle.com/sohamohajeri/recommendation-system-for-electronic-dataset

* https://towardsdatascience.com/extreme-deep-factorization-machine-xdeepfm-1ba180a6de78

* https://medium.com/building-creative-market/word2vec-inspired-recommendations-in-production-f2c6a6b5b0bf

* https://medium.com/shoprunner/fetching-better-beer-recommendations-with-collie-part-1-18c73ab30fbd

* https://towardsdatascience.com/deep-dive-into-netflixs-recommender-system-341806ae3b48

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

* [Instacart Market Basket Analysis. Winner’s Interview: 2nd place, Kazuki Onodera](https://medium.com/kaggle-blog/instacart-market-basket-analysis-feda2700cded)

* [Back From RecSys 2021](https://medium.com/@santiago_19815/back-from-recsys-2021-288c7fa40422)

* [Building a Multi-Stage Recommendation System (Part 1.1) `Dailymotion` `two tower`](https://medium.com/mlearning-ai/building-a-multi-stage-recommendation-system-part-1-1-95961ccf3dd8)

* [Building a multi-stage recommendation system (part 1.2) `Dailymotion` `two tower`](https://medium.com/mlearning-ai/building-a-multi-stage-recommendation-system-part-1-2-ce006f0825d1)

* [Beyond Recommendation Engines](https://medium.com/hellofresh-dev/beyond-recommendation-engines-a7ab13c14fa0)

* [Pinterest Home Feed Unified Lightweight Scoring: A Two-tower Approach](https://medium.com/pinterest-engineering/pinterest-home-feed-unified-lightweight-scoring-a-two-tower-approach-b3143ac70b55)

* [H&M Personalized Fashion Recommendations](https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/discussion/324070)

* https://medium.com/@sanchitgarg_78552/recommendations-for-h-m-ecommerce-ai-driven-retail-b0edde6746e6

* [Context-Aware Recommender Systems introduction: take SQN as an example](https://medium.com/@b08902022/context-aware-recommender-systems-introduction-take-sqn-as-an-example-453b6c66172)

* [Paper Review Monolith: Towards Better Recommendation Systems `TikTok`](https://pub.towardsai.net/paper-review-monolith-towards-better-recommendation-systems-b58702be416a)

* [Real-time customer behavior recommendations via session-based approach](https://becominghuman.ai/real-time-customer-behavior-recommendations-via-session-based-approach-50d9faf36877)

* [Personalized Fishbowl Recommendations with Learned Embeddings: Part 1 `Glassdoor`](https://medium.com/glassdoor-engineering/personalized-fishbowl-recommendations-with-learned-embeddings-part-1-6031abe84661)

* [Knowledge Graph Attention Network for Recommendation](https://medium.com/@shivanshsethi8821/knowledge-graph-attention-network-for-recommendation-4be007989076)

* [Training Larger and Faster Recommender Systems with PyTorch Sparse Embeddings](https://medium.com/nvidia-merlin/training-larger-and-faster-recommender-systems-with-pytorch-sparse-embeddings-53348a2cde3f)

* [End-to-End Recommender Systems with Merlin: Part 3](https://medium.com/@aryan.gupta18/end-to-end-recommender-systems-with-merlin-part-3-d0d3d7e30c43)

* [Recommendation Systems with Deep Learning](https://medium.com/google-cloud/recommendation-systems-with-deep-learning-69e5c1772571)

* [Pinterest Home Feed Unified Lightweight Scoring: A Two-tower Approach](https://medium.com/pinterest-engineering/pinterest-home-feed-unified-lightweight-scoring-a-two-tower-approach-b3143ac70b55)

* [Modern Recommender Systems](https://towardsdatascience.com/modern-recommender-systems-a0c727609aa8)

* [Semantic Label Representation with an Application on Multimodal Product Categorization](https://medium.com/walmartglobaltech/semantic-label-representation-with-an-application-on-multimodal-product-categorization-63d668b943b7)

* [Looking to build a recommendation system on Google Cloud? Leverage the following guidelines to identify the right solution for you (Part I)](https://cloud.google.com/blog/topics/developers-practitioners/looking-build-recommendation-system-google-cloud-leverage-following-guidelines-identify-right-solution-you-part-i)

* [Google: About recommendation models](https://cloud.google.com/retail/docs/models#si)

* ->[Learning product similarity in e-commerce using a supervised approach](https://towardsdatascience.com/learning-product-similarity-in-e-commerce-using-a-supervised-approach-525d734afd99)

* [Recommender System — Bayesian personalized ranking from implicit feedback](https://towardsdatascience.com/recommender-system-bayesian-personalized-ranking-from-implicit-feedback-78684bfcddf6)

* [10 Recommendation Techniques: Summary & Comparison](https://medium.com/@jchen001/10-recommendation-techniques-introduction-comparison-7ba4a3a2c940)

### RecSys series

#### by James Le

* [Part 1: An Executive Guide to Building Recommendation System](https://towardsdatascience.com/recommendation-system-series-part-1-an-executive-guide-to-building-recommendation-system-608f83e2630a)

* [Part 2: The 10 Categories of Deep Recommendation Systems That…](https://towardsdatascience.com/recommendation-system-series-part-2-the-10-categories-of-deep-recommendation-systems-that-189d60287b58)

* [Part 3: The 6 Research Directions of Deep Recommendation Systems That…](https://towardsdatascience.com/recommendation-system-series-part-3-the-6-research-directions-of-deep-recommendation-systems-that-3a328d264fb7)

* [Part 4: The 7 Variants of MF For Collaborative Filtering](https://towardsdatascience.com/recsys-series-part-4-the-7-variants-of-matrix-factorization-for-collaborative-filtering-368754e4fab5)

* [Part 5: The 5 Variants of MLP for Collaborative Filtering](https://towardsdatascience.com/recsys-series-part-5-neural-matrix-factorization-for-collaborative-filtering-a0aebfe15883)

* [Part 6: The 6 Variants of Autoencoders for Collaborative Filtering](https://towardsdatascience.com/recommendation-system-series-part-6-the-6-variants-of-autoencoders-for-collaborative-filtering-bd7b9eae2ec7)

* [Part 7: The 3 Variants of Boltzmann Machines for Collaborative Filtering](https://towardsdatascience.com/recsys-series-part-7-the-3-variants-of-boltzmann-machines-for-collaborative-filtering-4c002af258f9)

#### by Eugen Yan

* https://eugeneyan.com/tag/recsys/

* [RecSys 2020: Takeaways and Notable Papers](https://eugeneyan.com/writing/recsys2020/#towards-more-robust-offline-evaluation-and-study-reproducibility)

* [RecSys 2021: Papers and Talks to Chew on](https://eugeneyan.com/writing/recsys2021/)

* [RecSys 2022: Recap, Favorite Papers, and Lessons](https://eugeneyan.com/writing/recsys2022/)

* [Patterns for Personalization](https://eugeneyan.com/writing/patterns-for-personalization/)

#### by Wei Wei

* **Building recommendation systems with TensorFlow** - https://www.youtube.com/watch?v=RWlLaWMD30M&list=PLQY2H8rRoyvy2MiyUBz5RWZr5MPFkV3qz

### Algorithms

#### Deep learning for recsys

* [Recommender Systems using Deep Learning in PyTorch from scratch](https://towardsdatascience.com/recommender-systems-using-deep-learning-in-pytorch-from-scratch-f661b8f391d7)

* [Modern Recommender Systems. - A Deep Dive into the AI algorithms [Jun 2021]](https://towardsdatascience.com/modern-recommender-systems-a0c727609aa8)

* [pytorch-for-recommenders-101](https://blog.fastforwardlabs.com/2018/04/10/pytorch-for-recommenders-101.html) `Apr 2018`

* [Deep Learning Recommendation Models (DLRM) : A Deep Dive ](https://medium.com/swlh/deep-learning-recommendation-models-dlrm-a-deep-dive-f38a95f47c2c) `Oct 2020`

* [deep-learning-recommendation-models-dlrm-deep-dive ](https://www.kdnuggets.com/2021/04/deep-learning-recommendation-models-dlrm-deep-dive.html) `Apr 2021`

#### Probabilistic approach

* https://www.cs.toronto.edu/~amnih/papers/bpmf.pdf

* https://towardsdatascience.com/probabilistic-matrix-factorization-b7852244a321

* https://docs.pymc.io/notebooks/probabilistic_matrix_factorization.html

#### Implicit RecSys

* [Building (and Evaluating) a Recommender System for Implicit Feedback](https://medium.com/@judaikawa/building-and-evaluating-a-recommender-system-for-implicit-feedback-59495d2077d4)

* -> [Factorization Machines for Item Recommendation with Implicit Feedback Data](https://towardsdatascience.com/factorization-machines-for-item-recommendation-with-implicit-feedback-data-5655a7c749db)

#### Learn to Rank

* [Pointwise vs. Pairwise vs. Listwise Learning to Rank](https://medium.com/@nikhilbd/pointwise-vs-pairwise-vs-listwise-learning-to-rank-80a8fe8fadfd)

* [Advances in TF-Ranking](https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html)

#### Graph

* [Network models for recommender systems](https://medium.com/dunnhumby-data-science-engineering/network-models-for-recommender-systems-7f0d6d210ccf)

#### Reinforcement Learning

* -> [RL in RecSys, an overview](https://scitator.medium.com/rl-in-recsys-an-overview-e02815019a8f)

* [Build a reinforcement learning recommendation application using Vertex AI](https://cloud.google.com/blog/topics/developers-practitioners/build-reinforcement-learning-recommendation-application-using-vertex-ai)

* [Building Self learning Recommendation System using Reinforcement Learning : Part I](https://bayesianquest.com/2022/01/03/building-self-learning-recommendation-system-using-reinforcement-learning-part-i/)

* [Reinforcement learning in Recommender systems, with Kim Folk](https://www.youtube.com/watch?v=1a5YSU2ho_I)

#### Transformers

* [https://medium.com/genifyai/genify-transformer-model-recommender-system-6cd0c8414527](https://medium.com/genifyai/genify-transformer-model-recommender-system-6cd0c8414527)

#### Autoencoders

* [How Variational Autoencoders make classical recommender systems obsolete.](https://medium.com/snipfeed/how-variational-autoencoders-make-classical-recommender-systems-obsolete-4df8bae51546)

* [Implementation of deep generative models for recommender systems in Tensorflow🔮 Implementation of VAEs and GANs](https://medium.com/snipfeed/how-to-implement-deep-generative-models-for-recommender-systems-29110be8971f)

#### Hands-on

* https://taufik-azri.medium.com/recommendation-system-for-retail-customer-3f0f80b84221

* https://colab.research.google.com/github/google/eng-edu/blob/main/ml/recommendation-systems/recommendation-systems.ipynb

### Evaluation metrics for RecSys

* [Evaluation Metrics for Recommender Systems](https://towardsdatascience.com/evaluation-metrics-for-recommender-systems-df56c6611093)

* [MAP@k](http://sdsawtelle.github.io/blog/output/mean-average-precision-MAP-for-recommender-systems.html)

* -> [KDD 2021 Mixed Method Development of Evaluation Metrics](https://kdd2021-mixedmethods.github.io/)

* -> [KDD 2020 Tutorial on Online User Engagement](https://onlineuserengagement.github.io/)

* [RecSys 2020 Session P2A: Evaluating and Explaining Recommendations](https://www.youtube.com/watch?v=90xFB3qZxuI)

### RecSys in tech companies

#### OLX

* https://tech.olx.com/item2vec-neural-item-embeddings-to-enhance-recommendations-1fd948a6f293

#### DoorDash

* [**Simple logistic regression model for recommendation**](https://doordash.news/2017/07/07/powering-search-recommendations-at-doordash/)

* [**Store2Vec**](https://doordash.engineering/2018/04/02/personalized-store-feed-with-vector-embeddings/)

* [**Using Triplet Loss and Siamese Neural Networks to Train Catalog Item Embeddings**](https://doordash.engineering/2021/09/08/using-twin-neural-networks-to-train-catalog-item-embeddings/)

* [Things Not Strings](https://doordash.engineering/2020/12/15/understanding-search-intent-with-better-recall/)

* [**Personalized Cuisine Filter**](https://doordash.engineering/2020/01/27/personalized-cuisine-filter/)

#### Airbnb

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

* [Improving Deep Learning for Ranking Stays at Airbnb](https://medium.com/airbnb-engineering/improving-deep-learning-for-ranking-stays-at-airbnb-959097638bde)

  * [Applying Deep Learning To Airbnb Search](https://arxiv.org/pdf/1810.09591.pdf)

  * [Improving Deep Learning For Airbnb Search](https://arxiv.org/pdf/2002.05515.pdf)

  * [Managing Diversity in Airbnb Search](https://arxiv.org/pdf/2004.02621.pdf)

* [How we use automl multi task learning and multi tower models for pinterest ads](https://medium.com/pinterest-engineering/how-we-use-automl-multi-task-learning-and-multi-tower-models-for-pinterest-ads-db966c3dc99e)

* [Pinnersage multi modal user embedding framework for recommendations at pinterest](https://medium.com/pinterest-engineering/pinnersage-multi-modal-user-embedding-framework-for-recommendations-at-pinterest-bfd116b49475)

* [Driving shopping upsells from pinterest search](https://medium.com/pinterest-engineering/driving-shopping-upsells-from-pinterest-search-d06329255402)

* [Using pid controllers to diversify content types on home feed](https://medium.com/pinterest-engineering/using-pid-controllers-to-diversify-content-types-on-home-feed-1c7195c89218)

* [Searchsage learning search query representations at pinterest](https://medium.com/pinterest-engineering/searchsage-learning-search-query-representations-at-pinterest-654f2bb887fc)

* [Pinterest home feed unified lightweight scoring a two tower approach](https://medium.com/pinterest-engineering/pinterest-home-feed-unified-lightweight-scoring-a-two-tower-approach-b3143ac70b55)

* [The machine learning behind delivering relevant ads](https://medium.com/pinterest-engineering/the-machine-learning-behind-delivering-relevant-ads-8987fc5ba1c0)

* [Advertiser recommendation systems at pinterest](https://medium.com/pinterest-engineering/advertiser-recommendation-systems-at-pinterest-ccb255fbde20)

* [Detecting image similarity in near real time using apache flink](https://medium.com/pinterest-engineering/detecting-image-similarity-in-near-real-time-using-apache-flink-723ce072b7d2)

#### Pinteres

* [**Improving the Quality of Recommended Pins with Lightweight Ranking** (2020)](https://medium.com/pinterest-engineering/improving-the-quality-of-recommended-pins-with-lightweight-ranking-8ff5477b20e3)

* [**Advertiser Recommendation Systems at Pinterest**](https://medium.com/pinterest-engineering/advertiser-recommendation-systems-at-pinterest-ccb255fbde20)

#### Spotify

* -> [How Spotify Recommends Your New Favorite Artist (2019)](https://towardsdatascience.com/how-spotify-recommends-your-new-favorite-artist-8c1850512af0)

* [How does Spotify's recommendation system work?](https://www.univ.ai/post/spotify-recommendations)

#### Uber

* [Food Discovery with Uber Eats: Recommending for the Marketplace (2018)](https://eng.uber.com/uber-eats-recommending-marketplace/)

#### Facebook

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

#### fennel

* [Real World Recommendation System - Part 1](https://blog.fennel.ai/p/real-world-recommendation-system?s=r)

#### eBay

* [Multi-Objective Ranking for Promoted Auction Items `eBay`](https://medium.com/@ebaytechblog/multi-objective-ranking-for-promoted-auction-items-293bf204574f)

#### Expedia

* [How to Optimise Rankings with Cascade Bandits](ttps://medium.com/expedia-group-tech/how-to-optimise-rankings-with-cascade-bandits-5d92dfa0f16b)

#### Booking

* [Personalization in Practice - Booking workshop](https://booking.ai/personalization-in-practice-2bb4bc680eb3)

#### NVIDIA

* https://developer.nvidia.com/blog/how-to-build-a-winning-recommendation-system-part-1/

* https://developer.nvidia.com/blog/how-to-build-a-winning-recommendation-system-part-2-deep-learning-for-recommender-systems/

* [Nvidea Merlin](https://rapids.ai/merlin.html)

  * [Nvidia Merlin documentation ](https://developer.nvidia.com/nvidia-merlin)

  * [NVTabular doc](https://nvidia-merlin.github.io/NVTabular/main/index.html)

  * [NVIDIA Merlin vs TensorFlow Recommenders: A comparison of these recommendation frameworks](https://analyticsindiamag.com/nvidia-merlin-vs-tensorflow-recommenders-a-comparison-of-these-recommendation-frameworks/)

  * [Training Deep Learning Based Recommender Systems 9x Faster with TensorFlow](https://medium.com/nvidia-merlin/training-deep-learning-based-recommender-systems-9x-faster-with-tensorflow-cc5a2572ea49)

  * [GTC 2020: NVTabular: GPU Accelerated ETL for Recommender Systems](https://resources.nvidia.com/c/GTC2020-s21651?x=yBY2dT&lx=jsVNdg)

  * [](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/#)

  * [Overcoming Data Preprocessing Bottlenecks with TensorFlow Data Service, NVIDIA DALI, and Other Methods](https://towardsdatascience.com/overcoming-data-preprocessing-bottlenecks-with-tensorflow-data-service-nvidia-dali-and-other-d6321917f851)

  * [Accelerating ETL for Recommender Systems on NVIDIA GPUs with NVTabular](https://developer.nvidia.com/blog/accelerating-etl-for-recsys-on-gpus-with-nvtabular/)

  * [Scalable Recommender Systems with NVTabular- A Fast Tabular Data Loading and Transformation Library](https://medium.com/rapids-ai/gpu-recommender-systems-with-nvtabular-eee056c37ea0)

* Summit 2021:

  * https://developer.nvidia.com/blog/using-neural-networks-for-your-recommender-system/?ncid=progr-559712#cid=dl19_progr_en-us

  * https://developer.nvidia.com/blog/nvidia-earns-1st-place-in-recsys-challenge-2021/?ncid=progr-290013#cid=dl19_progr_en-us

  * https://www.nvidia.com/en-us/training/instructor-led-workshops/intelligent-recommender-systems/

  * https://developer.nvidia.com/nvidia-merlin?ncid=progr-101132#cid=dl19_progr_en-us

  * https://medium.com/nvidia-merlin/winning-the-recsys2021-challenge-by-a-diverse-set-of-xgboost-and-neural-network-models-4c5422a642d8

  * https://on24static.akamaized.net/event/32/96/53/0/rt/1/documents/resourceList1627506368727/nvidiarecsyssummit1627506366215.pdf

  * https://on24static.akamaized.net/event/32/96/53/0/rt/1/documents/resourceList1627506400731/deeplearningrecsysday21627506398728.pdf

  * https://on24static.akamaized.net/event/32/96/53/0/rt/1/documents/resourceList1627506417526/tensorflowrecommendersnvidia11627506415818.pdf

* [Recommender Systems Summit 2022](https://www.youtube.com/watch?v=9rouLchcC0k&t=97s)

  

* [**Recommender Systems, Not Just Recommender Models (four stages)**](https://medium.com/nvidia-merlin/recommender-systems-not-just-recommender-models-485c161c755e)

#### Google

* [Matrix factorization with BQML](https://medium.com/google-cloud/how-to-build-a-recommendation-system-on-e-commerce-data-using-bigquery-ml-df9af2b8c110)

* [**Find anything blazingly fast with Google's vector search technology**](https://cloud.google.com/blog/topics/developers-practitioners/find-anything-blazingly-fast-googles-vector-search-technology)

* [Looking to build a recommendation system on Google Cloud? Leverage the following guidelines to identify the right solution for you (Part I)](https://cloud.google.com/blog/topics/developers-practitioners/looking-build-recommendation-system-google-cloud-leverage-following-guidelines-identify-right-solution-you-part-i)

#### AWS

* [**What’s new in recommender systems**](https://aws.amazon.com/blogs/media/whats-new-in-recommender-systems/)

## Videos

* -> [Personalizing Explainable Recommendations with Multi-objective Contextual Bandits (Spotify)](https://www.youtube.com/watch?v=KoMKgNeUX4k)

* -> [MORS: Workshop on Multi-Objective Recommender Systems](https://www.youtube.com/watch?v=CeNfq6_VX2s)

* [**Shared Neural Item Representations for Completely Cold Start Problem**](https://www.youtube.com/watch?v=oLsdP47K8qc)

* [Maciej Kula | Neural Networks for Recommender Systems](https://www.youtube.com/watch?v=ZkBQ6YA9E40)

* [Building Production Recommender Systems - Maciej Kula - WEB2DAY 2017](https://www.youtube.com/watch?v=CLNFmm6Lj_I)

* [ Building AI-based Recommendation Systems, a value-based approach - Xiquan Cui ](https://www.youtube.com/watch?v=Ax_TNqJrR5s&list=PLRJL9rjwb40l0FFb4loYysgqob_GYImp-)

* [ Introduction to the OTTO competition on Kaggle (RecSys) ](https://www.youtube.com/watch?v=gtPEX_eRAVo)

* [ Rishabh Mehrotra: Recommendations in a Marketplace (part 1) ](https://www.youtube.com/watch?v=sx9lKCCeWoc)

* [ Rishabh Mehrotra: Recommendations in a Marketplace (part 2) ](https://www.youtube.com/watch?v=3gPYN61ZGAI)

## Online Courses

* Recommender Systems and Deep Learning in Python - https://www.udemy.com/course/recommender-systems/

* Building Recommender Systems with Machine Learning and AI - https://www.udemy.com/course/building-recommender-systems-with-machine-learning-and-ai/

* Google course for RecSys - https://developers.google.com/machine-learning/recommendation

* ACM Summer School on Recommender Systems 2017 - http://pro.unibz.it/projects/schoolrecsys17/program.html

* Recommender Systems Specialization (University of Minnesota) - https://www.coursera.org/specializations/recommender-systems

* Build an ML Recommender System - https://www.manning.com/liveproject/build-an-ML-recommender-system

* Workshops

  * Workshop on context-awere recommendation system - https://cars-workshop.com/cars-2021

* [Machine Learning Recommender System With Python 2022 `Data Academy`](https://www.youtube.com/playlist?list=PLsugXK9b1w1nlDH0rbxIufJLeC3MsbRaa)

* [Personalized Recommendations at Scale](https://corise.com/course/personalized-recommendation-at-scale)

## Books

* [Practical Recommender Systems](https://www.manning.com/books/practical-recommender-systems)

* [Recommender Systems Handbook 2rd ed. 2011 Edition](https://raw.githubusercontent.com/melissakou/Recommender-Systems-Handbook/main/%5BBook%5D%20Recommender%20Systems%20Handbook.pdf)

* [Recommender Systems Handbook 3rd ed. 2022 Edition](https://www.amazon.com/Recommender-Systems-Handbook-Francesco-Ricci/dp/1071621963)

* [Recommender Systems Textbook 1er ed 2016 Edition](https://www.amazon.com/Recommender-Systems-Textbook-Charu-Aggarwal-dp-3319296574/dp/3319296574/ref=mt_other?_encoding=UTF8&me=&qid=)

* [Recommendation Engines](https://www.amazon.com/Recommendation-Engines-Press-Essential-Knowledge/dp/0262539071)

* [Recommender Systems: An Introduction](https://www.amazon.com/Recommender-Systems-Introduction-Dietmar-Jannach/dp/0521493366)

* [Personalized Machine Learning](https://cseweb.ucsd.edu/~jmcauley/pml/pml_book.pdf)

 

## Code

### Implementations

* https://github.com/lyst/lightfm

* https://github.com/benfred/implicit

* https://github.com/maciejkula/spotlight

* https://github.com/shenweichen/DeepCTR

* https://github.com/etlundquist/rankfm

* https://github.com/tensorflow/recommenders [quick start](https://www.tensorflow.org/recommenders/examples/quickstart)

* https://github.com/jfkirk/tensorrec

* https://github.com/tensorflow/ranking/

* https://github.com/RUCAIBox/RecBole

* https://github.com/ShopRunner/collie_recs/

* https://github.com/metarank/metarank

* https://github.com/linkedin/detext

* https://github.com/PreferredAI/cornac/

### competition and hands-on

* https://github.com/hojinYang/spotify_recSys_challenge_2018

* -> [tfrs-movierec-serving](https://github.com/hojinYang/tfrs-movierec-serving)

* [recsys_autoencoders](https://github.com/marlesson/recsys_autoencoders)

* [Build a recommendation system with TensorFlow and Keras `two tower`](https://github.com/xei/recommender-system-tutorial/blob/main/recommender_system_tutorial.ipynb)

* [WSDM Cup on Cross-Market Recommendation Competition](https://xmrec.github.io/wsdmcup/)

## Datasets

* https://www.kaggle.com/retailrocket/ecommerce-dataset

* https://gist.github.com/entaroadun/1653794

* https://github.com/RUCAIBox/RecSysDatasets

* 30music / impresions / tv audience - https://recsys.deib.polimi.it/datasets/

* http://archive.ics.uci.edu/ml/datasets/KASANDR

## Papers

[Connected papers](https://www.connectedpapers.com/)

[ACM](https://dl.acm.org/topic/conference-collections/recsys?)

* **OpenTable recommendations (2015)** - https://www.slideshare.net/BuhwanJeong/deep-learning-c-43529709

* 2001

  * [Item-Based Collaborative Filtering Recommendation Algorithms `GroupLens`](https://dl.acm.org/doi/pdf/10.1145/371920.372071)

* 2004

  * [Item-based top-N recommendation algorithms](https://dl.acm.org/doi/10.1145/963770.963776)

* 2008

  * [Collaborative Filtering for Implicit Feedback Datasets `AT&T`](http://yifanhu.net/PUB/cf.pdf)

* 2009

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

  * [A Survey of Accuracy Evaluation Metrics of Recommendation Tasks `Evaluation` `Microsoft`](https://dl.acm.org/doi/10.5555/1577069.1755883)

* 2010

  * [Collaborative filtering meets mobile recommendation: a user-centered approach `Microsoft`](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/AAAI10-Collaborative20Filtering20Meets20Mobile20Recommendation20A20User-centered20Approach-1.pdf)

  * [Factorization Machine (2010)](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf)

* 2011

  * [SLIM: Sparse Linear Methods for Top-N Recommender Systems (2011)](http://glaros.dtc.umn.edu/gkhome/fetch/papers/SLIM2011icdm.pdf) 

  * [Adaptive bootstrapping of recommender systems using decision trees](https://dl.acm.org/doi/10.1145/1935826.1935910)

* 2012

  * [A Topic-based Recommender System for Electronic Marketplace Platforms `Content-base`](https://ieeexplore.ieee.org/document/6495071)

* 2013

  * [**Deep content-based music recommendation (2013 Spotify)**](https://papers.nips.cc/paper/2013/file/b3ba8f1bee1238a2f37603d90b58898d-Paper.pdf)

* 2015

  * [A Probabilistic Model for Using Social Networks in Personalized Item Recommendation](https://dl.acm.org/doi/10.1145/2792838.2800193)

  * [AutoRec: Autoencoders Meet Collaborative Filtering (2015)`Autoencoder`](https://users.cecs.anu.edu.au/~akmenon/papers/autorec/autorec-paper.pdf)

  * [Metadata Embeddings for User and Item Cold-start Recommendations `Lyst` (LightFM)](https://arxiv.org/pdf/1507.08439.pdf)

  * -> [The Netflix Recommender System: Algorithms, Business Value and Innovation (2015 Netflix)](https://dl.acm.org/doi/pdf/10.1145/2843948)

* 2016

  * [Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features `Microsoft`](https://dl.acm.org/doi/10.1145/2939672.2939704)

  * [Collaborative Denoising Auto-Encoders for Top-N Recommender Systems Dato `Autoencoder`](https://dl.acm.org/doi/pdf/10.1145/2835776.2835837)

  * [**Item2Vec: Neural Item Embedding for Collaborative Filtering `Microsoft`**](https://arxiv.org/abs/1603.04259)

  * [Session-based Recommendations with Recurrent Neural Networks `Yusp` `Telefonica` `Netflix` `Session-base`](https://arxiv.org/pdf/1511.06939.pdf)

  * [A Neural Autoregressive Approach to Collaborative Filtering `Autoencoder`](https://arxiv.org/abs/1605.09477)

  * [**Prod2vec: E-commerce in Your Inbox: Product Recommendations at Scale `Yahoo`**](https://arxiv.org/pdf/1606.07154.pdf)

  * [**Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation `Criteo`**](https://arxiv.org/pdf/1607.07326.pdf)

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

  * [**Deep Neural Networks for YouTube Recommendations `Google`**](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/45530.pdf)

  * [Recommendations as Treatments: Debiasing Learning and Evaluation](http://proceedings.mlr.press/v48/schnabel16.pdf)

  * -> [Local Item-Item Models For Top-N Recommendation](https://dl.acm.org/doi/pdf/10.1145/2959100.2959185)

  * [A Generic Coordinate Descent Framework for Learning from Implicit Feedback `Google`](https://arxiv.org/pdf/1611.04666.pdf)

  * [CB2CF: A Neural Multiview Content-to-Collaborative Filtering Model for Completely Cold Item Recommendations `Microsoft`](https://arxiv.org/abs/1611.00384)  

* 2017

  * [Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems `Evaluation`](https://dl.acm.org/doi/10.1145/2926720)

  * [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://arxiv.org/abs/1701.04783)

  * [Sequential User-based Recurrent Neural Network Recommendations (2017)](https://dl.acm.org/doi/pdf/10.1145/3109859.3109877)

  * [Neural Collaborative Filtering (2017)](https://arxiv.org/pdf/1708.05031.pdf)

  * [Deep & Cross Network for Ad Click Predictions (2017 Google)](https://arxiv.org/pdf/1708.05123.pdf)

  * [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (2017 Huawei)](https://arxiv.org/pdf/1703.04247.pdf)

  * [Deep & Cross Network for Ad Click Predictions V1 (2017 Google)](https://arxiv.org/pdf/1708.05123.pdf)

  * [Embedding-based News Recommendation for Millions of Users (Yahoo 2017)](http://library.usc.edu.ph/ACM/KKD%202017/pdfs/p1933.pdf)

  * [Folding: Why Good Models Sometimes Make Spurious Recommendations (2017 Google)](https://dl.acm.org/doi/pdf/10.1145/3109859.3109911)

  * [Collaborative Variational Autoencoder for Recommender Systems (2017)`Autoencoder`](https://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf)

  * [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations (2017 Yusp-Telefonica)](https://arxiv.org/pdf/1706.03847.pdf)

  * [Related Pins at Pinterest: The Evolution of a Real-World Recommender System `Pinterest`](https://arxiv.org/abs/1702.07969) 

  * [ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation `Alibaba`](https://arxiv.org/abs/1711.06632)

  * [Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales](https://arxiv.org/abs/1708.06520)

  * [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/abs/1708.04617)

  * [Deep Embedding Forest: Forest-based Serving with Deep Embedding Features `Microsoft`](https://arxiv.org/abs/1703.05291)

  * [Collaborative Metric Learning `Evaluation`](https://dl.acm.org/doi/10.1145/3038912.3052639)

* 2018

  * [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems (2018 Microsoft)](https://arxiv.org/pdf/1803.05170.pdf)

  * [Latent Cross: Making Use of Context in Recurrent Recommender Systems (2018 Google)](https://dl.acm.org/doi/pdf/10.1145/3159652.3159727)

  * [Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (2018 Google)](https://dl.acm.org/doi/pdf/10.1145/3219819.3220007)

  * [Practical Diversified Recommendations on YouTube with Determinantal Point Processes (2018 Google)](https://dl.acm.org/doi/pdf/10.1145/3269206.3272018)

  * [Explore, exploit, and explain: personalizing explainable recommendations with bandits (2018 Spotify)](https://dl.acm.org/doi/pdf/10.1145/3240323.3240354)

  * [MULTI-VAE: Variational Autoencoders for Collaborative Filtering (2018 Netflix/Google)`Autoencoder`](https://arxiv.org/pdf/1802.05814.pdf)

  * [Practical Diversified Recommendations on YouTube with Determinantal Point Processes (2018 Google)](https://dl.acm.org/doi/pdf/10.1145/3269206.3272018)

  * [Adversarial Collaborative Auto-encoder for Top-N Recommendation (2018)](https://arxiv.org/pdf/1808.05361.pdf)

  * [Causal Embeddings for Recommendation]()

  * [Sequence-Aware Recommender Systems](https://arxiv.org/abs/1802.08452)

  * [Offline A/B testing for Recommender Systems `Criteo`](https://arxiv.org/abs/1801.07030)

  * [**Deep neural network marketplace recommenders in online experiments `Schibsted`**](https://arxiv.org/abs/1809.02130) / [**Five lessons from building a deep neural network recommender**](https://arxiv.org/abs/1809.02131)

  * [Learning Item-Interaction Embeddings for User Recommendations `Etsy`](https://arxiv.org/abs/1812.04407)

  * [Word2Vec applied to Recommendation: Hyperparameters Matter `Deezer`](https://arxiv.org/abs/1804.04212)

  * [Calibrated recommendations `Evaluation` `Netflix`](https://dl.acm.org/doi/10.1145/3240323.3240372)

  

* 2019 

  * [Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019 Facebook)](https://arxiv.org/pdf/1906.00091.pdf)

  * [**Sampling-bias-corrected neural modeling for large corpus item recommendations (2019 Google)`Two tower`**](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/6c8a86c981a62b0126a11896b7f6ae0dae4c3566.pdf)

    - [**PS6: Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations - Yi et al. (ACM)**](https://www.youtube.com/watch?v=O4cqDdtflnY)

    - [PR-282: Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations](https://www.youtube.com/watch?v=FSDuo9ybv8s)

  * [Recommending what video to watch next: a multitask ranking system (2019 Google)](https://dl.acm.org/doi/pdf/10.1145/3298689.3346997)

  * [SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning (2019 Google)](https://ojs.aaai.org/index.php/AAAI/article/view/3788)

  * [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer (2019)](https://dl.acm.org/doi/abs/10.1145/3357384.3357895)

  * [Improving Relevance Prediction with Transfer Learning in Large-scale Retrieval Systems (2019 Google)](https://openreview.net/pdf?id=SJxPVcSonN)

  * [Building a Recommender System Using Embeddings (2019 Drop)](https://drop.engineering/building-a-recommender-system-using-embeddings-de5a30e655aa)

  * [End-to-End Retrieval in Continuous Space (2019 Google) `Two tower`](https://arxiv.org/pdf/1811.08008.pdf)

  * [Beyond Greedy Ranking: Slate Optimization via List-CVAE (2019)`Autoencoder`](https://arxiv.org/pdf/1803.01682.pdf)

  * [On the Difficulty of Evaluating Baselines (2019 Google)`evaluation`](https://arxiv.org/pdf/1905.01395.pdf)

  * [**Are we really making much progress? A worrying analysis of recent neural recommendation approaches** (2019)`evaluation`](https://dl.acm.org/doi/pdf/10.1145/3298689.3347058)

  * [**Embarrassingly Shallow Autoencoders for Sparse Data** (2019 Netflix)`autoencoder`](https://arxiv.org/abs/1905.03375)

  * [Collaborative Filtering via High-Dimensional Regression (2019 Netflix)](https://arxiv.org/pdf/1904.13033.pdf)

  * [RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback (2019 Samsung)](https://arxiv.org/pdf/1912.11160.pdf)

  * [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba (2019 Alibaba)](https://arxiv.org/pdf/1905.06874.pdf)

  * [**Managing Popularity Bias in Recommender Systems with Personalized Re-ranking** (2019)](https://arxiv.org/pdf/1901.07555.pdf)

  * [KGAT: Knowledge Graph Attention Network for Recommendation](https://arxiv.org/abs/1905.07854)

  * [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921)

  * [Recommending what video to watch next: a multitask ranking system `Google`](https://daiwk.github.io/assets/youtube-multitask.pdf)

  * [PAL: a position-bias aware learning framework for CTR prediction in live recommender systems `Huawei`](https://www.researchgate.net/publication/335771749_PAL_a_position-bias_aware_learning_framework_for_CTR_prediction_in_live_recommender_systems)

* 2020

  * [SSE-PT: Sequential Recommendation Via Personalized Transformer](https://dl.acm.org/doi/10.1145/3383313.3412258)

  * [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems (2020 Google)](https://arxiv.org/pdf/2008.13535.pdf)

  * [Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations (2020 Tencet)](https://dl.acm.org/doi/pdf/10.1145/3383313.3412236)

  * [Contextual and Sequential User Embeddings for Large-Scale Music Recommendation (2020)](https://dl.acm.org/doi/pdf/10.1145/3383313.3412248)

  * -> [P-Companion: A Principled Framework for Diversified Complementary Product Recommendation (2020 Aamzon)](https://dl.acm.org/doi/pdf/10.1145/3340531.3412732)

    * [Improving complementary-product recommendations `Amazon`](https://www.amazon.science/blog/improving-complementary-product-recommendations)

  * [Pre-training Tasks for Embedding-based Large-scale Retrieval (2020 Google)](https://arxiv.org/pdf/2002.03932.pdf)

  * [Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations (2020 Google)`two tower`](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/b9f4e78a8830fe5afcf2f0452862fb3c0d6584ea.pdf)

  * [On sampled metrics for item recommendation (2020 Google)](https://dl.acm.org/doi/pdf/10.1145/3394486.3403226)

  * [Temporal-Contextual Recommendation in Real-Time (2020 Amazon)](https://dl.acm.org/doi/pdf/10.1145/3394486.3403278)

  * [**Neural Collaborative Filtering vs. Matrix Factorization Revisited `Google`**](https://dl.acm.org/doi/pdf/10.1145/3383313.3412488)

  * [An Embedding Learning Framework for Numerical Features in CTR Prediction](https://arxiv.org/pdf/2012.08986.pdf)

  * [Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations (ByteDance)](https://arxiv.org/pdf/2007.07203v2.pdf)

  * [Embedding-based Retrieval in Facebook Search `Facebook` `Two tower`](https://dl.acm.org/doi/pdf/10.1145/3394486.3403305)

  * -> [Causal Inference for Recommender Systems](https://dl.acm.org/doi/10.1145/3383313.3412225)

  * [Off-policy Learning in Two-stage Recommender Systems `Two tower`](https://dl.acm.org/doi/abs/10.1145/3366423.3380130)

  * [How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead `Coveo`](https://aclanthology.org/2020.ecnlp-1.2/)

  * [Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario `Coveo`](https://arxiv.org/abs/2007.14906)

  * [RecoBERT: A Catalog Language Model for Text-Based Recommendations `Micorsoft`](https://arxiv.org/abs/2009.13292)

  * [Attentive Item2Vec: Neural Attentive User Representations `Microsoft`](https://arxiv.org/abs/2002.06205)

  * [Neural Interactive Collaborative Filtering](https://arxiv.org/abs/2007.02095)

  

  

* 2021

  * [Exploring Heterogeneous Metadata for Video Recommendation with Two-tower Model (2021 Amazon)](https://arxiv.org/pdf/2109.11059.pdf)

  * [Theoretical Understandings of Product Embedding for E-commerce Machine Learning (2021 Walmart)](https://arxiv.org/pdf/2102.12029.pdf)

  * [**Self-supervised Learning for Large-scale Item Recommendations (2021 Google)** `two tower`](https://arxiv.org/pdf/2007.12865.pdf 	)

  * [Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees? (2021 Google ICRL)](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/a7f0822e77e8b6b4c00c879707fe60e3955d4a03.pdf)

  * [**Item Recommendation from Implicit Feedback (2021 Google)**](https://arxiv.org/pdf/2101.08769.pdf)

  * [A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research (2021)`review`](https://arxiv.org/pdf/1911.07698.pdf)

  * [One Person, One Model, One World: Learning Continual User Representation without Forgetting (2021)](https://arxiv.org/pdf/2009.13724.pdf)

  * [Towards Source-Aligned Variational Models for Cross-Domain Recommendation `autoencoder`](https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=7433&context=sis_research )

  * [**Negative Interactions for Improved Collaborative Filtering: Don’t go Deeper, go Higher (Netflix)**](https://dl.acm.org/doi/pdf/10.1145/3460231.3474273)

  * -> [Shared Neural Item Representations for Completely Cold Start Problem ()](https://dl.acm.org/doi/abs/10.1145/3460231.3474228)

  * [Reenvisioning the comparison between Neural Collaborative Filtering and Matrix Factorization](https://dl.acm.org/doi/abs/10.1145/3460231.3475944)

  * [**Graph Learning based Recommender Systems: A Review**`review`](https://arxiv.org/pdf/2105.06339.pdf)

  * [Reinforcement learning based recommender systems: A survey`survey`](https://arxiv.org/pdf/2101.06286.pdf)

  * [**Recommendations as Treatments**](https://ojs.aaai.org/index.php/aimagazine/article/view/18141/18875)

  * [**Deep Learning for Recommender Systems: A Netflix Case Study** `Netflix`](https://ojs.aaai.org/index.php/aimagazine/article/view/18140/18876)

  * [Query2Prod2Vec: Grounded Word Embeddings for eCommerce `Coveo`](https://aclanthology.org/2021.naacl-industry.20/)

  * [A/B Testing for Recommender Systems in a Two-sided Marketplace `Linedin`](https://arxiv.org/abs/2106.00762)

  * [A Constrained Optimization Approach for Calibrated Recommendations `Evaluation`](https://dl.acm.org/doi/fullHtml/10.1145/3460231.3478857)

* 2022

  * [Cross Pairwise Ranking for Unbiased Item Recommendation](https://arxiv.org/pdf/2204.12176v1.pdf)

  * [Towards Universal Sequence Representation Learning for Recommender Systems](https://arxiv.org/abs/2206.05941)[Code](https://github.com/rucaibox/unisrec)

  * [Weighing dynamic availability and consumption for Twitch recommendations (Amazon)](https://www.amazon.science/publications/weighing-dynamic-availability-and-consumption-for-twitch-recommendations)

  * [ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest](https://arxiv.org/pdf/2205.11728.pdf)

  * [**A Brief History of Recommender Systems `Review`**](https://arxiv.org/abs/2209.01860)

* 2023

  * [Calibrated Recommendations as a Minimum-Cost Flow Problem `Evaluation` `Spotify`](https://dl.acm.org/doi/10.1145/3539597.3570402)

### Papers by topics

tower model 

* [Learning Text Similarity with Siamese Recurrent Networks (2016 textkernel)](https://aclanthology.org/W16-1617.pdf)

* [Smart Reply: Automated Response Suggestion for Email (2016 Google)](https://arxiv.org/pdf/1606.04870v1.pdf)

* [Learning Semantic Textual Similarity from Conversations - (2018 Google)](https://arxiv.org/pdf/1804.07754.pdf)

Product search recommendation

* [A Transformer-based Embedding Model for Personalized Product Search (2020)](https://arxiv.org/pdf/2005.08936.pdf)

eCommerce

* [Predicting Shopping Behavior with Mixture of RNNs (2017 Rakuten)](https://difabbrizio.com/papers/sigir-ecom-2017-cs.pdf)

* [Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario (2020 Coveo)](https://arxiv.org/pdf/2007.14906.pdf)

* [Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution (2020 Coveo)](https://arxiv.org/pdf/2007.10087.pdf)

* [How to Grow a (Product) Tree Personalized Category Suggestions for eCommerce Type-Ahead (2020 Coveo)](https://aclanthology.org/2020.ecnlp-1.2.pdf)

* [Shopper intent prediction from clickstream e‑commerce data with minimal browsing information (2020)](https://www.nature.com/articles/s41598-020-73622-y.pdf)

## Recsys Conference

* [ACM Recsys](https://recsys.acm.org/)

* [ACM Recsys conference](https://dl.acm.org/conference/recsys)

* [ACM SIGIR Conference on Research and Development in Information Retrieval](https://sigir.org/sigir2022/)

* [ACM International Conference on Information and Knowledge Management - CIKM](https://www.cikm2021.org/)

### Videos

RecSys 2020 (https://slideslive.com/acmrecsys)

* [**A Human Perspective on Algorithmic Similarity** `Netflix`](https://slideslive.com/38934788)

* [**Balancing Relevance and Discovery to Inspire Customers in the IKEA App** `IKEA`](https://slideslive.com/38934789)

* [**Behavior-based Popularity Ranking on Amazon Video** `Amazon`](https://slideslive.com/38934790)

* [**Building a Reciprocal Recommendation System at Scale From Scratch: Learnings from One of Japan's Prominent Dating Applications** `Tapple`](https://slideslive.com/38934791)

* [Counterfactual Learning for Recommender System `Huawei`](https://slideslive.com/38934792)

* [Developing Recommendation System to provide a Personalized Learning experience at Chegg `Chegg`](https://slideslive.com/38934793)

* [Investigating Multimodal Features for Video Recommendations at Globoplay `Globoplay`](https://slideslive.com/38934794)

* [On the Heterogeneous Information Needs in the Job Domain: A Unified Platform for Student Career `Talto`](https://slideslive.com/38934795)

* [Query as Context for Item-to-Item Recommendation `Etsy`](https://slideslive.com/38934796)

* [The Embeddings that Came in From the Cold: Improving Vectors for New and Rare Products with Content-Based Inference `Coveo`](https://slideslive.com/38934797)

## Other Awesone list

* https://github.com/hongleizhang/RSPapers

* https://github.com/wzhe06/Reco-papers

* https://github.com/robi56/Deep-Learning-for-Recommendation-Systems

* https://paperswithcode.com/task/recommendation-systems?page=2

* https://github.com/microsoft/recommenders

* https://github.com/guyulongcs/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising

* https://github.com/scnu-dil/awesome-RecSys

* [SIGIR (Special Interest Group on Information Retrieval)](https://sigir-ecom.github.io/)

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

# Profesors

* [Thorsten Joachims, Cornell University](https://www.cs.cornell.edu/people/tj/)