Awesome-recsys

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

1. Blogs post
2. Videos
3. Online Courses
4. Books
5. Code
6. Datasets
7. Papers
8. 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
• Instacart Market Basket Analysis. Winner’s Interview: 2nd place, Kazuki Onodera
• Back From RecSys 2021
• Building a Multi-Stage Recommendation System (Part 1.1) Dailymotion two tower
• Building a multi-stage recommendation system (part 1.2) Dailymotion two tower
• Beyond Recommendation Engines
• Pinterest Home Feed Unified Lightweight Scoring: A Two-tower Approach
• H&M Personalized Fashion Recommendations
• https://medium.com/@sanchitgarg_78552/recommendations-for-h-m-ecommerce-ai-driven-retail-b0edde6746e6
• Context-Aware Recommender Systems introduction: take SQN as an example
• Paper Review Monolith: Towards Better Recommendation Systems TikTok
• Real-time customer behavior recommendations via session-based approach
• Personalized Fishbowl Recommendations with Learned Embeddings: Part 1 Glassdoor
• Knowledge Graph Attention Network for Recommendation
• Training Larger and Faster Recommender Systems with PyTorch Sparse Embeddings
• End-to-End Recommender Systems with Merlin: Part 3
• Recommendation Systems with Deep Learning
• Pinterest Home Feed Unified Lightweight Scoring: A Two-tower Approach
• Modern Recommender Systems
• Semantic Label Representation with an Application on Multimodal Product Categorization
• Looking to build a recommendation system on Google Cloud? Leverage the following guidelines to identify the right solution for you (Part I)
• Google: About recommendation models
• ->Learning product similarity in e-commerce using a supervised approach
• Recommender System — Bayesian personalized ranking from implicit feedback
• 10 Recommendation Techniques: Summary & Comparison

RecSys series

by James Le

• Part 1: An Executive Guide to Building Recommendation System
• Part 2: The 10 Categories of Deep Recommendation Systems That…
• Part 3: The 6 Research Directions of Deep Recommendation Systems That…
• Part 4: The 7 Variants of MF For Collaborative Filtering
• Part 5: The 5 Variants of MLP for Collaborative Filtering
• Part 6: The 6 Variants of Autoencoders for Collaborative Filtering
• Part 7: The 3 Variants of Boltzmann Machines for Collaborative Filtering

by Eugen Yan

• https://eugeneyan.com/tag/recsys/
• RecSys 2020: Takeaways and Notable Papers
• RecSys 2021: Papers and Talks to Chew on
• RecSys 2022: Recap, Favorite Papers, and Lessons
• 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
• Modern Recommender Systems. - A Deep Dive into the AI algorithms [Jun 2021]
• pytorch-for-recommenders-101 Apr 2018
• Deep Learning Recommendation Models (DLRM) : A Deep Dive Oct 2020
• deep-learning-recommendation-models-dlrm-deep-dive 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
• -> Factorization Machines for Item Recommendation with Implicit Feedback Data

Learn to Rank

• Pointwise vs. Pairwise vs. Listwise Learning to Rank
• Advances in TF-Ranking

Graph

• Network models for recommender systems

Reinforcement Learning

• -> RL in RecSys, an overview
• Build a reinforcement learning recommendation application using Vertex AI
• Building Self learning Recommendation System using Reinforcement Learning : Part I
• Reinforcement learning in Recommender systems, with Kim Folk

Transformers

• https://medium.com/genifyai/genify-transformer-model-recommender-system-6cd0c8414527

Autoencoders

• How Variational Autoencoders make classical recommender systems obsolete.
• Implementation of deep generative models for recommender systems in Tensorflow🔮 Implementation of VAEs and GANs

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
• MAP@k
• -> KDD 2021 Mixed Method Development of Evaluation Metrics
• -> KDD 2020 Tutorial on Online User Engagement
• RecSys 2020 Session P2A: Evaluating and Explaining Recommendations

RecSys in tech companies

OLX

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

DoorDash

• Simple logistic regression model for recommendation
• Store2Vec
• Using Triplet Loss and Siamese Neural Networks to Train Catalog Item Embeddings
• Things Not Strings
• Personalized Cuisine Filter

Airbnb

• Listing Embeddings in Search Ranking
• Improving Deep Learning for Ranking Stays at Airbnb
  • Applying Deep Learning To Airbnb Search
  • Improving Deep Learning For Airbnb Search
  • Managing Diversity in Airbnb Search
• How we use automl multi task learning and multi tower models for pinterest ads
• Pinnersage multi modal user embedding framework for recommendations at pinterest
• Driving shopping upsells from pinterest search
• Using pid controllers to diversify content types on home feed
• Searchsage learning search query representations at pinterest
• Pinterest home feed unified lightweight scoring a two tower approach
• The machine learning behind delivering relevant ads
• Advertiser recommendation systems at pinterest
• Detecting image similarity in near real time using apache flink

Pinteres

• Improving the Quality of Recommended Pins with Lightweight Ranking (2020)
• Advertiser Recommendation Systems at Pinterest

Spotify

• -> How Spotify Recommends Your New Favorite Artist (2019)
• How does Spotify's recommendation system work?

Uber

• Food Discovery with Uber Eats: Recommending for the Marketplace (2018)

Facebook

• Powered by AI: Instagram’s Explore recommender system

fennel

• Real World Recommendation System - Part 1

eBay

• Multi-Objective Ranking for Promoted Auction Items eBay

Expedia

• How to Optimise Rankings with Cascade Bandits

Booking

• Personalization in Practice - Booking workshop

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

  • Nvidia Merlin documentation
  • NVTabular doc
  • NVIDIA Merlin vs TensorFlow Recommenders: A comparison of these recommendation frameworks
  • Training Deep Learning Based Recommender Systems 9x Faster with TensorFlow
  • GTC 2020: NVTabular: GPU Accelerated ETL for Recommender Systems
  • Overcoming Data Preprocessing Bottlenecks with TensorFlow Data Service, NVIDIA DALI, and Other Methods
  • Accelerating ETL for Recommender Systems on NVIDIA GPUs with NVTabular
  • Scalable Recommender Systems with NVTabular- A Fast Tabular Data Loading and Transformation Library

• 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

• Recommender Systems, Not Just Recommender Models (four stages)

Google

• Matrix factorization with BQML
• Find anything blazingly fast with Google's 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)

AWS

• What’s new in recommender systems

Videos

• -> Personalizing Explainable Recommendations with Multi-objective Contextual Bandits (Spotify)
• -> MORS: Workshop on Multi-Objective Recommender Systems
• Shared Neural Item Representations for Completely Cold Start Problem
• Maciej Kula | Neural Networks for Recommender Systems
• Building Production Recommender Systems - Maciej Kula - WEB2DAY 2017
• Building AI-based Recommendation Systems, a value-based approach - Xiquan Cui
• Introduction to the OTTO competition on Kaggle (RecSys)
• Rishabh Mehrotra: Recommendations in a Marketplace (part 1)
• Rishabh Mehrotra: Recommendations in a Marketplace (part 2)

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
• Personalized Recommendations at Scale

Books

• Practical Recommender Systems
• Recommender Systems Handbook 2rd ed. 2011 Edition
• Recommender Systems Handbook 3rd ed. 2022 Edition
• Recommender Systems Textbook 1er ed 2016 Edition
• Recommendation Engines
• Recommender Systems: An Introduction
• Personalized Machine Learning

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://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
• recsys_autoencoders
• Build a recommendation system with TensorFlow and Keras two tower
• WSDM Cup on Cross-Market Recommendation Competition

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
• yambda https://huggingface.co/datasets/yandex/yambda

Papers

Connected papers

ACM

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

• 2001

  • Item-Based Collaborative Filtering Recommendation Algorithms GroupLens

• 2004

  • Item-based top-N recommendation algorithms

• 2008

  • Collaborative Filtering for Implicit Feedback Datasets AT&T

• 2009

  • BPR: Bayesian Personalized Ranking from Implicit Feedback
  • A Survey of Accuracy Evaluation Metrics of Recommendation Tasks Evaluation Microsoft

• 2010

  • Collaborative filtering meets mobile recommendation: a user-centered approach Microsoft
  • Factorization Machine (2010)

• 2011

  • SLIM: Sparse Linear Methods for Top-N Recommender Systems (2011)
  • Adaptive bootstrapping of recommender systems using decision trees

• 2012

  • A Topic-based Recommender System for Electronic Marketplace Platforms Content-base

• 2013

  • Deep content-based music recommendation (2013 Spotify)

• 2015

  • A Probabilistic Model for Using Social Networks in Personalized Item Recommendation
  • AutoRec: Autoencoders Meet Collaborative Filtering (2015)Autoencoder
  • Metadata Embeddings for User and Item Cold-start Recommendations Lyst (LightFM)
  • -> The Netflix Recommender System: Algorithms, Business Value and Innovation (2015 Netflix)

• 2016

  • Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features Microsoft
  • Collaborative Denoising Auto-Encoders for Top-N Recommender Systems Dato Autoencoder
  • Item2Vec: Neural Item Embedding for Collaborative Filtering Microsoft
  • Session-based Recommendations with Recurrent Neural Networks Yusp Telefonica Netflix Session-base
  • A Neural Autoregressive Approach to Collaborative Filtering Autoencoder
  • Prod2vec: E-commerce in Your Inbox: Product Recommendations at Scale Yahoo
  • Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation Criteo
  • Wide & Deep Learning for Recommender Systems Google
  • Deep Neural Networks for YouTube Recommendations Google
  • Recommendations as Treatments: Debiasing Learning and Evaluation
  • -> Local Item-Item Models For Top-N Recommendation
  • A Generic Coordinate Descent Framework for Learning from Implicit Feedback Google
  • CB2CF: A Neural Multiview Content-to-Collaborative Filtering Model for Completely Cold Item Recommendations Microsoft

• 2017

  • Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems Evaluation
  • Joint Deep Modeling of Users and Items Using Reviews for Recommendation
  • Sequential User-based Recurrent Neural Network Recommendations (2017)
  • Neural Collaborative Filtering (2017)
  • Deep & Cross Network for Ad Click Predictions (2017 Google)
  • DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (2017 Huawei)
  • Deep & Cross Network for Ad Click Predictions V1 (2017 Google)
  • Embedding-based News Recommendation for Millions of Users (Yahoo 2017)
  • Folding: Why Good Models Sometimes Make Spurious Recommendations (2017 Google)
  • Collaborative Variational Autoencoder for Recommender Systems (2017)Autoencoder
  • Recurrent Neural Networks with Top-k Gains for Session-based Recommendations (2017 Yusp-Telefonica)
  • Related Pins at Pinterest: The Evolution of a Real-World Recommender System Pinterest
  • ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation Alibaba
  • Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales
  • Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
  • Deep Embedding Forest: Forest-based Serving with Deep Embedding Features Microsoft
  • Collaborative Metric Learning Evaluation

• 2018

  • xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems (2018 Microsoft)
  • Latent Cross: Making Use of Context in Recurrent Recommender Systems (2018 Google)
  • Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (2018 Google)
  • Practical Diversified Recommendations on YouTube with Determinantal Point Processes (2018 Google)
  • Explore, exploit, and explain: personalizing explainable recommendations with bandits (2018 Spotify)
  • MULTI-VAE: Variational Autoencoders for Collaborative Filtering (2018 Netflix/Google)Autoencoder
  • Practical Diversified Recommendations on YouTube with Determinantal Point Processes (2018 Google)
  • Adversarial Collaborative Auto-encoder for Top-N Recommendation (2018)
  • Causal Embeddings for Recommendation
  • Sequence-Aware Recommender Systems
  • Offline A/B testing for Recommender Systems Criteo
  • Deep neural network marketplace recommenders in online experiments Schibsted / Five lessons from building a deep neural network recommender
  • Learning Item-Interaction Embeddings for User Recommendations Etsy
  • Word2Vec applied to Recommendation: Hyperparameters Matter Deezer
  • Calibrated recommendations Evaluation Netflix

• 2019

  • Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019 Facebook)
  • Sampling-bias-corrected neural modeling for large corpus item recommendations (2019 Google)Two tower
    • PS6: Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations - Yi et al. (ACM)
    • PR-282: Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations
  • Recommending what video to watch next: a multitask ranking system (2019 Google)
  • SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning (2019 Google)
  • BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer (2019)
  • Improving Relevance Prediction with Transfer Learning in Large-scale Retrieval Systems (2019 Google)
  • Building a Recommender System Using Embeddings (2019 Drop)
  • End-to-End Retrieval in Continuous Space (2019 Google) Two tower
  • Beyond Greedy Ranking: Slate Optimization via List-CVAE (2019)Autoencoder
  • On the Difficulty of Evaluating Baselines (2019 Google)evaluation
  • Are we really making much progress? A worrying analysis of recent neural recommendation approaches (2019)evaluation
  • Embarrassingly Shallow Autoencoders for Sparse Data (2019 Netflix)autoencoder
  • Collaborative Filtering via High-Dimensional Regression (2019 Netflix)
  • RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback (2019 Samsung)
  • Behavior Sequence Transformer for E-commerce Recommendation in Alibaba (2019 Alibaba)
  • Managing Popularity Bias in Recommender Systems with Personalized Re-ranking (2019)
  • KGAT: Knowledge Graph Attention Network for Recommendation
  • AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
  • Recommending what video to watch next: a multitask ranking system Google
  • PAL: a position-bias aware learning framework for CTR prediction in live recommender systems Huawei

• 2020

  • SSE-PT: Sequential Recommendation Via Personalized Transformer
  • DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems (2020 Google)
  • Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations (2020 Tencet)
  • Contextual and Sequential User Embeddings for Large-Scale Music Recommendation (2020)
  • -> P-Companion: A Principled Framework for Diversified Complementary Product Recommendation (2020 Aamzon)
    • Improving complementary-product recommendations Amazon
  • Pre-training Tasks for Embedding-based Large-scale Retrieval (2020 Google)
  • Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations (2020 Google)two tower
  • On sampled metrics for item recommendation (2020 Google)
  • Temporal-Contextual Recommendation in Real-Time (2020 Amazon)
  • Neural Collaborative Filtering vs. Matrix Factorization Revisited Google
  • An Embedding Learning Framework for Numerical Features in CTR Prediction
  • Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations (ByteDance)
  • Embedding-based Retrieval in Facebook Search Facebook Two tower
  • -> Causal Inference for Recommender Systems
  • Off-policy Learning in Two-stage Recommender Systems Two tower
  • How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead Coveo
  • Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario Coveo
  • RecoBERT: A Catalog Language Model for Text-Based Recommendations Micorsoft
  • Attentive Item2Vec: Neural Attentive User Representations Microsoft
  • Neural Interactive Collaborative Filtering

• 2021

  • Exploring Heterogeneous Metadata for Video Recommendation with Two-tower Model (2021 Amazon)
  • Theoretical Understandings of Product Embedding for E-commerce Machine Learning (2021 Walmart)
  • Self-supervised Learning for Large-scale Item Recommendations (2021 Google) two tower
  • Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees? (2021 Google ICRL)
  • Item Recommendation from Implicit Feedback (2021 Google)
  • A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research (2021)review
  • One Person, One Model, One World: Learning Continual User Representation without Forgetting (2021)
  • Towards Source-Aligned Variational Models for Cross-Domain Recommendation autoencoder
  • Negative Interactions for Improved Collaborative Filtering: Don’t go Deeper, go Higher (Netflix)
  • -> Shared Neural Item Representations for Completely Cold Start Problem ()
  • Reenvisioning the comparison between Neural Collaborative Filtering and Matrix Factorization
  • Graph Learning based Recommender Systems: A Reviewreview
  • Reinforcement learning based recommender systems: A surveysurvey
  • Recommendations as Treatments
  • Deep Learning for Recommender Systems: A Netflix Case Study Netflix
  • Query2Prod2Vec: Grounded Word Embeddings for eCommerce Coveo
  • A/B Testing for Recommender Systems in a Two-sided Marketplace Linedin
  • A Constrained Optimization Approach for Calibrated Recommendations Evaluation

• 2022

  • Cross Pairwise Ranking for Unbiased Item Recommendation
  • Towards Universal Sequence Representation Learning for Recommender SystemsCode
  • Weighing dynamic availability and consumption for Twitch recommendations (Amazon)
  • ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest
  • A Brief History of Recommender Systems Review

• 2023

  • Calibrated Recommendations as a Minimum-Cost Flow Problem Evaluation Spotify

Papers by topics

tower model

• Learning Text Similarity with Siamese Recurrent Networks (2016 textkernel)
• Smart Reply: Automated Response Suggestion for Email (2016 Google)
• Learning Semantic Textual Similarity from Conversations - (2018 Google)

Product search recommendation

• A Transformer-based Embedding Model for Personalized Product Search (2020)

eCommerce

• Predicting Shopping Behavior with Mixture of RNNs (2017 Rakuten)
• Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario (2020 Coveo)
• Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution (2020 Coveo)
• How to Grow a (Product) Tree Personalized Category Suggestions for eCommerce Type-Ahead (2020 Coveo)
• Shopper intent prediction from clickstream e‑commerce data with minimal browsing information (2020)

Recsys Conference

• ACM Recsys
• ACM Recsys conference
• ACM SIGIR Conference on Research and Development in Information Retrieval
• ACM International Conference on Information and Knowledge Management - CIKM

Videos

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

• A Human Perspective on Algorithmic Similarity Netflix
• Balancing Relevance and Discovery to Inspire Customers in the IKEA App IKEA
• Behavior-based Popularity Ranking on Amazon Video Amazon
• Building a Reciprocal Recommendation System at Scale From Scratch: Learnings from One of Japan's Prominent Dating Applications Tapple
• Counterfactual Learning for Recommender System Huawei
• Developing Recommendation System to provide a Personalized Learning experience at Chegg Chegg
• Investigating Multimodal Features for Video Recommendations at Globoplay Globoplay
• On the Heterogeneous Information Needs in the Job Domain: A Unified Platform for Student Career Talto
• Query as Context for Item-to-Item Recommendation Etsy
• The Embeddings that Came in From the Cold: Improving Vectors for New and Rare Products with Content-Based Inference Coveo

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)
• Papers with code

Profesors

• Thorsten Joachims, Cornell University