Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
Awesome-recsys
Curated list of recommnedation system topics
https://github.com/creyesp/Awesome-recsys
Last synced: 3 days ago
JSON representation
-
Papers
-
competition and hands-on
- Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations (2020 Tencet)
- On sampled metrics for item recommendation (2020 Google)
- Building a Recommender System Using Embeddings (2019 Drop)
- ACM
- Item-Based Collaborative Filtering Recommendation Algorithms `GroupLens`
- Item-based top-N recommendation algorithms
- A Survey of Accuracy Evaluation Metrics of Recommendation Tasks `Evaluation` `Microsoft`
- Adaptive bootstrapping of recommender systems using decision trees
- A Probabilistic Model for Using Social Networks in Personalized Item Recommendation
- The Netflix Recommender System: Algorithms, Business Value and Innovation (2015 Netflix)
- Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features `Microsoft`
- Collaborative Denoising Auto-Encoders for Top-N Recommender Systems Dato `Autoencoder`
- Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems `Evaluation`
- Sequential User-based Recurrent Neural Network Recommendations (2017)
- 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`
- xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems (2018 Microsoft)
- Latent Cross: Making Use of Context in Recurrent Recommender Systems (2018 Google)
- Practical Diversified Recommendations on YouTube with Determinantal Point Processes (2018 Google)
- Explore, exploit, and explain: personalizing explainable recommendations with bandits (2018 Spotify)
- Adversarial Collaborative Auto-encoder for Top-N Recommendation (2018)
- Sequence-Aware Recommender Systems
- Offline A/B testing for Recommender Systems `Criteo`
- **Deep neural network marketplace recommenders in online experiments `Schibsted`**
- Learning Item-Interaction Embeddings for User Recommendations `Etsy`
- Word2Vec applied to Recommendation: Hyperparameters Matter `Deezer`
- Calibrated recommendations `Evaluation` `Netflix`
- 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)
- **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`
- 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)
- Contextual and Sequential User Embeddings for Large-Scale Music Recommendation (2020)
- Improving complementary-product recommendations `Amazon`
- Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations (2020 Google)`two tower`
- 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
- 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
- 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`
- Cross Pairwise Ranking for Unbiased Item Recommendation
- Towards Universal Sequence Representation Learning for Recommender Systems
- 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`**
- Calibrated Recommendations as a Minimum-Cost Flow Problem `Evaluation` `Spotify`
- **Wide & Deep Learning for Recommender Systems `Google`**
- MULTI-VAE: Variational Autoencoders for Collaborative Filtering (2018 Netflix/Google)`Autoencoder`
- Building a Recommender System Using Embeddings (2019 Drop)
- Behavior Sequence Transformer for E-commerce Recommendation in Alibaba (2019 Alibaba)
- Building a Recommender System Using Embeddings (2019 Drop)
- Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations (ByteDance)
- Connected papers
- Collaborative Filtering for Implicit Feedback Datasets `AT&T`
- BPR: Bayesian Personalized Ranking from Implicit Feedback
- Collaborative filtering meets mobile recommendation: a user-centered approach `Microsoft`
- Factorization Machine (2010)
- SLIM: Sparse Linear Methods for Top-N Recommender Systems (2011)
- A Topic-based Recommender System for Electronic Marketplace Platforms `Content-base`
- **Deep content-based music recommendation (2013 Spotify)**
- AutoRec: Autoencoders Meet Collaborative Filtering (2015)`Autoencoder`
- Metadata Embeddings for User and Item Cold-start Recommendations `Lyst` (LightFM)
- **Item2Vec: Neural Item Embedding for Collaborative Filtering `Microsoft`**
- Session-based Recommendations with Recurrent Neural Networks `Yusp` `Telefonica` `Netflix` `Session-base`
- Joint Deep Modeling of Users and Items Using Reviews for Recommendation
- 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`
- DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (2017 Huawei)
- Building a Recommender System Using Embeddings (2019 Drop)
- **Self-supervised Learning for Large-scale Item Recommendations (2021 Google)** `two tower`
- Building a Recommender System Using Embeddings (2019 Drop)
- Exploring Heterogeneous Metadata for Video Recommendation with Two-tower Model (2021 Amazon)
- Neural Collaborative Filtering (2017)
- Building a Recommender System Using Embeddings (2019 Drop)
- Building a Recommender System Using Embeddings (2019 Drop)
-
Papers by topics
- Shopper intent prediction from clickstream e‑commerce data with minimal browsing information (2020)
- Learning Text Similarity with Siamese Recurrent Networks (2016 textkernel)
- Smart Reply: Automated Response Suggestion for Email (2016 Google)
- A Transformer-based Embedding Model for Personalized Product Search (2020)
- 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)
- Shopper intent prediction from clickstream e‑commerce data with minimal browsing information (2020)
- Shopper intent prediction from clickstream e‑commerce data with minimal browsing information (2020)
- Shopper intent prediction from clickstream e‑commerce data with minimal browsing information (2020)
- Shopper intent prediction from clickstream e‑commerce data with minimal browsing information (2020)
- Shopper intent prediction from clickstream e‑commerce data with minimal browsing information (2020)
- Shopper intent prediction from clickstream e‑commerce data with minimal browsing information (2020)
-
-
Blogs post
-
RecSys series
- 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
- 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
- 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
- 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
- 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
- RecSys 2020: Takeaways and Notable Papers
- RecSys 2021: Papers and Talks to Chew on
- RecSys 2022: Recap, Favorite Papers, and Lessons
- Patterns for Personalization
- 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
- 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
- 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
- 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
-
- Beyond Recommendation Engines
- Paper Review Monolith: Towards Better Recommendation Systems `TikTok`
- Real-time customer behavior recommendations via session-based approach
- Learning product similarity in e-commerce using a supervised approach
- Recommender System — Bayesian personalized ranking from implicit feedback
- Beyond Recommendation Engines
- Paper Review Monolith: Towards Better Recommendation Systems `TikTok`
- Real-time customer behavior recommendations via session-based approach
- Learning product similarity in e-commerce using a supervised approach
- Recommender System — Bayesian personalized ranking from implicit feedback
- Beyond Recommendation Engines
- Paper Review Monolith: Towards Better Recommendation Systems `TikTok`
- Real-time customer behavior recommendations via session-based approach
- Modern Recommender Systems
- Beyond Recommendation Engines
- Paper Review Monolith: Towards Better Recommendation Systems `TikTok`
- Real-time customer behavior recommendations via session-based approach
- Learning product similarity in e-commerce using a supervised approach
- Recommender System — Bayesian personalized ranking from implicit feedback
- Beyond Recommendation Engines
- Paper Review Monolith: Towards Better Recommendation Systems `TikTok`
- Real-time customer behavior recommendations via session-based approach
- Learning product similarity in e-commerce using a supervised approach
- Recommender System — Bayesian personalized ranking from implicit feedback
- 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
- H&M Personalized Fashion Recommendations
- 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
- Semantic Label Representation with an Application on Multimodal Product Categorization
- 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
- Beyond Recommendation Engines
- Paper Review Monolith: Towards Better Recommendation Systems `TikTok`
- Real-time customer behavior recommendations via session-based approach
- Learning product similarity in e-commerce using a supervised approach
- Recommender System — Bayesian personalized ranking from implicit feedback
- Beyond Recommendation Engines
- Paper Review Monolith: Towards Better Recommendation Systems `TikTok`
- Real-time customer behavior recommendations via session-based approach
- Learning product similarity in e-commerce using a supervised approach
- Recommender System — Bayesian personalized ranking from implicit feedback
- Beyond Recommendation Engines
- Paper Review Monolith: Towards Better Recommendation Systems `TikTok`
- Real-time customer behavior recommendations via session-based approach
- Learning product similarity in e-commerce using a supervised approach
- Recommender System — Bayesian personalized ranking from implicit feedback
- Learning product similarity in e-commerce using a supervised approach
- Recommender System — Bayesian personalized ranking from implicit feedback
- Beyond Recommendation Engines
- Paper Review Monolith: Towards Better Recommendation Systems `TikTok`
- Real-time customer behavior recommendations via session-based approach
-
Algorithms
- Modern Recommender Systems. - A Deep Dive into the AI algorithms [Jun 2021
- Recommender Systems using Deep Learning in PyTorch from scratch
- Factorization Machines for Item Recommendation with Implicit Feedback Data
- Recommender Systems using Deep Learning in PyTorch from scratch
- Modern Recommender Systems. - A Deep Dive into the AI algorithms [Jun 2021
- Factorization Machines for Item Recommendation with Implicit Feedback Data
- Modern Recommender Systems. - A Deep Dive into the AI algorithms [Jun 2021
- Recommender Systems using Deep Learning in PyTorch from scratch
- Factorization Machines for Item Recommendation with Implicit Feedback Data
- Recommender Systems using Deep Learning in PyTorch from scratch
- Modern Recommender Systems. - A Deep Dive into the AI algorithms [Jun 2021
- Factorization Machines for Item Recommendation with Implicit Feedback Data
- Modern Recommender Systems. - A Deep Dive into the AI algorithms [Jun 2021
- Recommender Systems using Deep Learning in PyTorch from scratch
- pytorch-for-recommenders-101
- Deep Learning Recommendation Models (DLRM) : A Deep Dive
- deep-learning-recommendation-models-dlrm-deep-dive
- Building (and Evaluating) a Recommender System for Implicit Feedback
- Factorization Machines for Item Recommendation with Implicit Feedback Data
- Pointwise vs. Pairwise vs. Listwise Learning to Rank
- Advances in TF-Ranking
- Network models for recommender systems
- 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
- https://medium.com/genifyai/genify-transformer-model-recommender-system-6cd0c8414527
- How Variational Autoencoders make classical recommender systems obsolete.
- Implementation of deep generative models for recommender systems in Tensorflow🔮 Implementation of VAEs and GANs
- Modern Recommender Systems. - A Deep Dive into the AI algorithms [Jun 2021
- Recommender Systems using Deep Learning in PyTorch from scratch
- Factorization Machines for Item Recommendation with Implicit Feedback Data
- Modern Recommender Systems. - A Deep Dive into the AI algorithms [Jun 2021
- Recommender Systems using Deep Learning in PyTorch from scratch
- Factorization Machines for Item Recommendation with Implicit Feedback Data
- Modern Recommender Systems. - A Deep Dive into the AI algorithms [Jun 2021
- Recommender Systems using Deep Learning in PyTorch from scratch
- Factorization Machines for Item Recommendation with Implicit Feedback Data
- Modern Recommender Systems. - A Deep Dive into the AI algorithms [Jun 2021
- Recommender Systems using Deep Learning in PyTorch from scratch
- Factorization Machines for Item Recommendation with Implicit Feedback Data
-
Evaluation metrics for RecSys
- Evaluation Metrics for Recommender Systems
- Evaluation Metrics for Recommender Systems
- Evaluation Metrics for Recommender Systems
- Evaluation Metrics for Recommender Systems
- 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
- Evaluation Metrics for Recommender Systems
- Evaluation Metrics for Recommender Systems
- Evaluation Metrics for Recommender Systems
- Evaluation Metrics for Recommender Systems
-
RecSys in tech companies
- How Spotify Recommends Your New Favorite Artist (2019)
- Personalization in Practice - Booking workshop
- Overcoming Data Preprocessing Bottlenecks with TensorFlow Data Service, NVIDIA DALI, and Other Methods
- How Spotify Recommends Your New Favorite Artist (2019)
- Personalization in Practice - Booking workshop
- Overcoming Data Preprocessing Bottlenecks with TensorFlow Data Service, NVIDIA DALI, and Other Methods
- How Spotify Recommends Your New Favorite Artist (2019)
- Personalization in Practice - Booking workshop
- Overcoming Data Preprocessing Bottlenecks with TensorFlow Data Service, NVIDIA DALI, and Other Methods
- How Spotify Recommends Your New Favorite Artist (2019)
- Personalization in Practice - Booking workshop
- Overcoming Data Preprocessing Bottlenecks with TensorFlow Data Service, NVIDIA DALI, and Other Methods
- Pinterest home feed unified lightweight scoring a two tower approach
- **Simple logistic regression model for recommendation**
- **Store2Vec**
- **Using Triplet Loss and Siamese Neural Networks to Train Catalog Item Embeddings**
- Using pid controllers to diversify content types on home feed
- Things Not Strings
- **Personalized Cuisine Filter**
- **Listing Embeddings in Search Ranking**
- Improving Deep Learning for Ranking Stays at Airbnb
- Applying Deep Learning To 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
- Searchsage learning search query representations at pinterest
- The machine learning behind delivering relevant ads
- **Advertiser Recommendation Systems at Pinterest**
- How does Spotify's recommendation system work?
- Detecting image similarity in near real time using apache flink
- **Improving the Quality of Recommended Pins with Lightweight Ranking** (2020)
- How Spotify Recommends Your New Favorite Artist (2019)
- Food Discovery with Uber Eats: Recommending for the Marketplace (2018)
- Real World Recommendation System - Part 1
- Multi-Objective Ranking for Promoted Auction Items `eBay`
- Personalization in Practice - Booking workshop
- 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
- Recommender Systems Summit 2022
- **Recommender Systems, Not Just Recommender Models (four stages)**
- **Find anything blazingly fast with Google's vector search technology**
- Matrix factorization with BQML
- Looking to build a recommendation system on Google Cloud? Leverage the following guidelines to identify the right solution for you (Part I)
- **What’s new in recommender systems**
- Overcoming Data Preprocessing Bottlenecks with TensorFlow Data Service, NVIDIA DALI, and Other Methods
- How Spotify Recommends Your New Favorite Artist (2019)
- Personalization in Practice - Booking workshop
- Overcoming Data Preprocessing Bottlenecks with TensorFlow Data Service, NVIDIA DALI, and Other Methods
- How Spotify Recommends Your New Favorite Artist (2019)
- Personalization in Practice - Booking workshop
- Overcoming Data Preprocessing Bottlenecks with TensorFlow Data Service, NVIDIA DALI, and Other Methods
- How Spotify Recommends Your New Favorite Artist (2019)
- Personalization in Practice - Booking workshop
- How Spotify Recommends Your New Favorite Artist (2019)
- Personalization in Practice - Booking workshop
- Overcoming Data Preprocessing Bottlenecks with TensorFlow Data Service, NVIDIA DALI, and Other Methods
-
-
Recsys Conference
-
Papers by topics
-
Videos
- **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
-
Videos
-
RecSys in tech companies
- 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
-
RecSys in tech companies
-
-
Books
-
Code
-
Implementations
-
competition and hands-on
-
Categories