"Recommender systems are often characterized as tools that help users in their decision-making process"
- 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)
Dailymotiontwo tower - Building a multi-stage recommendation system (part 1.2)
Dailymotiontwo 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
- 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
- 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
- Building recommendation systems with TensorFlow - https://www.youtube.com/watch?v=RWlLaWMD30M&list=PLQY2H8rRoyvy2MiyUBz5RWZr5MPFkV3qz
- 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
- 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
- Building (and Evaluating) a Recommender System for Implicit Feedback
- -> Factorization Machines for Item Recommendation with Implicit Feedback Data
- -> 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
- How Variational Autoencoders make classical recommender systems obsolete.
- Implementation of deep generative models for recommender systems in Tensorflow🔮 Implementation of VAEs and GANs
- 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 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
- 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
- Listing Embeddings in Search Ranking
- Improving Deep Learning for Ranking Stays at Airbnb
- 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
- Improving the Quality of Recommended Pins with Lightweight Ranking (2020)
- Advertiser Recommendation Systems at Pinterest
- -> How Spotify Recommends Your New Favorite Artist (2019)
- How does Spotify's recommendation system work?
- How to Optimise Rankings with Cascade Bandits
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https://developer.nvidia.com/blog/how-to-build-a-winning-recommendation-system-part-1/
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- 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
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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
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Recommender Systems, Not Just Recommender Models (four stages)
- 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)
- -> 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)
- 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
- 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
- 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/
- 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
- 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
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OpenTable recommendations (2015) - https://www.slideshare.net/BuhwanJeong/deep-learning-c-43529709
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2001
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2004
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2008
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2009
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2010
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2011
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2012
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2013
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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)
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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
YuspTelefonicaNetflixSession-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
- Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features
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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
- Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems
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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
EvaluationNetflix
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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 - 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
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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)
- 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
FacebookTwo 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
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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 Review
review - Reinforcement learning based recommender systems: A survey
survey - 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
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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
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2023
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
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)
- ACM Recsys
- ACM Recsys conference
- ACM SIGIR Conference on Research and Development in Information Retrieval
- ACM International Conference on Information and Knowledge Management - CIKM
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
- 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
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