Difference between revisions of "Alphabet"
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== Papers == | == Papers == | ||
| + | # [https://dl.acm.org/doi/pdf/10.1145/3651170 Efficient Optimization of Sparse User Encoder Recommenders], ACM Transactions on Recommender Systems, 2024 | ||
| + | # [https://dl.acm.org/doi/pdf/10.1145/3604915.3608882 Efficient Data Representation Learning in Google-scale Systems], [[RecSys 2023]] | ||
| + | # [https://dl.acm.org/doi/pdf/10.1145/3604915.3608792 Online Matching: A Real-time Bandit System for Large-scale Recommendations], RecSys 2023 | ||
| + | # [https://dl.acm.org/doi/pdf/10.1145/3600097 On Reducing User Interaction Data for Personalization], [[ACM TORS]], 2023 | ||
| + | # [https://arxiv.org/pdf/2306.01720.pdf Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation], [[KDD 2023]] | ||
| + | # [https://arxiv.org/abs/2305.06474 Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction], ??? 2023 | ||
| + | # [https://arxiv.org/abs/2305.05065 Recommender Systems with Generative Retrieval], ??? 2023 | ||
# {{ops}} [https://research.google/pubs/pub51712/ Data Management Principles], book chapter in ''Reliable Machine Learning: Applying SRE Principles to ML in Production'', 2022 | # {{ops}} [https://research.google/pubs/pub51712/ Data Management Principles], book chapter in ''Reliable Machine Learning: Applying SRE Principles to ML in Production'', 2022 | ||
| − | # {{neural}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/827afbd792b84f20bf1b439d1d678e121c9cfa46.pdf Scale Calibration of Deep Ranking Models], [[ | + | # [https://research.google/pubs/pub51652/ Surrogate for Long-Term User Experience in Recommender Systems], [[KDD 2022]] |
| + | # {{neural}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/827afbd792b84f20bf1b439d1d678e121c9cfa46.pdf Scale Calibration of Deep Ranking Models], KDD 2022 | ||
| + | # {{ltor}}{{neural}}{{ops}} [https://arxiv.org/pdf/2209.05310.pdf On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models], [[RecSys 2022]] | ||
# [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/bfbc205383e9fc0aa132011c587d5f826ba90274.pdf Bootstrapping Recommendations at Chrome Web Store], [[KDD 2021]] | # [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/bfbc205383e9fc0aa132011c587d5f826ba90274.pdf Bootstrapping Recommendations at Chrome Web Store], [[KDD 2021]] | ||
# {{ops}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/0d556e45afc54afeb2eb6b51a9bc1827b9961ff4.pdf “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI], [[CHI 2021]] | # {{ops}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/0d556e45afc54afeb2eb6b51a9bc1827b9961ff4.pdf “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI], [[CHI 2021]] | ||
| Line 16: | Line 25: | ||
# {{explanations}} [https://dl.acm.org/doi/pdf/10.1145/3397271.3401032 Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations], [[RecSys 2021]] | # {{explanations}} [https://dl.acm.org/doi/pdf/10.1145/3397271.3401032 Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations], [[RecSys 2021]] | ||
# [https://arxiv.org/pdf/2101.08769.pdf Item Recommendation from Implicit Feedback], 2021-01-21 – write-up on item recommendation from positive-only feedback with a focus on algorithms; no experiments, no dealing with bias | # [https://arxiv.org/pdf/2101.08769.pdf Item Recommendation from Implicit Feedback], 2021-01-21 – write-up on item recommendation from positive-only feedback with a focus on algorithms; no experiments, no dealing with bias | ||
| − | # [https://research.google/pubs/pub49284/ Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems] | + | # [https://research.google/pubs/pub49284/ Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems], [[WSDM 2021]] |
| − | # {{bandits}} | + | # {{bandits}} [https://proceedings.neurips.cc/paper/2020/hash/9b7c8d13e4b2f08895fb7bcead930b46-Abstract.html Latent Bandits Revisited], [[NeurIPS 2020]] |
# [https://papers.nips.cc/paper/2020/file/070dbb6024b5ef93784428afc71f2146-Paper.pdf Rankmax: An Adaptive Projection Alternative to the Softmax Function], [[NeurIPS 2020]] | # [https://papers.nips.cc/paper/2020/file/070dbb6024b5ef93784428afc71f2146-Paper.pdf Rankmax: An Adaptive Projection Alternative to the Softmax Function], [[NeurIPS 2020]] | ||
# {{performance}} [http://proceedings.mlr.press/v108/han20b.html MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search], [[AISTATS 2020]] | # {{performance}} [http://proceedings.mlr.press/v108/han20b.html MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search], [[AISTATS 2020]] | ||
| Line 62: | Line 71: | ||
# [https://cloud.google.com/blog/topics/developers-practitioners/building-large-scale-recommenders-using-cloud-tpus Building Large Scale Recommenders using Cloud TPUs], 2022-10-07 | # [https://cloud.google.com/blog/topics/developers-practitioners/building-large-scale-recommenders-using-cloud-tpus Building Large Scale Recommenders using Cloud TPUs], 2022-10-07 | ||
| + | # [https://blog.youtube/inside-youtube/on-youtubes-recommendation-system/ On YouTube's recommendation system], 2021-09-15 | ||
# [https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html Advances in TF-Ranking], 2021-07-21 | # [https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html Advances in TF-Ranking], 2021-07-21 | ||
# [https://scholar.googleblog.com/2021/02/scholar-recommendations-reloaded.html Scholar Recommendations Reloaded! Fresher, More Relevant, Easier], 2021-02-12 | # [https://scholar.googleblog.com/2021/02/scholar-recommendations-reloaded.html Scholar Recommendations Reloaded! Fresher, More Relevant, Easier], 2021-02-12 | ||
Latest revision as of 06:32, 13 March 2024
Alphabet is the parent company of Google and many of its (former) subsidiaries, for example YouTube and DeepMind.
Tutorials
Papers
- Efficient Optimization of Sparse User Encoder Recommenders, ACM Transactions on Recommender Systems, 2024
- Efficient Data Representation Learning in Google-scale Systems, RecSys 2023
- Online Matching: A Real-time Bandit System for Large-scale Recommendations, RecSys 2023
- On Reducing User Interaction Data for Personalization, ACM TORS, 2023
- Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation, KDD 2023
- Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction, ??? 2023
- Recommender Systems with Generative Retrieval, ??? 2023
- 🔩 Data Management Principles, book chapter in Reliable Machine Learning: Applying SRE Principles to ML in Production, 2022
- Surrogate for Long-Term User Experience in Recommender Systems, KDD 2022
- 🧠 Scale Calibration of Deep Ranking Models, KDD 2022
- 📑🧠🔩 On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models, RecSys 2022
- Bootstrapping Recommendations at Chrome Web Store, KDD 2021
- 🔩 “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI, CHI 2021
- 📑🧠 Are Neural Rankers Still Outperformed By Gradient Boosted Decision Trees?, ICLR 2021
- 🔑 Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations, RecSys 2021
- Item Recommendation from Implicit Feedback, 2021-01-21 – write-up on item recommendation from positive-only feedback with a focus on algorithms; no experiments, no dealing with bias
- Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems, WSDM 2021
- 🎰 Latent Bandits Revisited, NeurIPS 2020
- Rankmax: An Adaptive Projection Alternative to the Softmax Function, NeurIPS 2020
- 🏃 MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search, AISTATS 2020
- 📑 Interpretable Learning-to-Rank with Generalized Additive Models, 2020
- Off-policy Learning in Two-stage Recommender Systems, WWW 2020
- 🧠 Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations, WWW 2020; Google Play
- 🤔 Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies, KDD 2020
- 🧠 Neural Collaborative Filtering vs. Matrix Factorization Revisited, RecSys 2020
- Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval, CIKM 2020
- 📑 DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems, 2020
- Template:Runtime Accelerating Large-Scale Inference with Anisotropic Vector Quantization, 2019
- Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology, 2019
- Seq2Slate: Re-ranking and Slate Optimization with RNNs, 2019
- 🧠 Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks, SIGIR 2019 (short paper)
- 🧠Towards Neural Mixture Recommender for Long Range Dependent User Sequences, WWW 2019
- Top-K Off-Policy Correction for a REINFORCE Recommender System, WSDM 2019
- 🧠 Towards neural mixture recommender for long range dependent user sequences, WWW 2019
- 📑 Recommending what video to watch next: A multitask ranking system, RecSys 2019
- 🤔 Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations, RecSys 2019
- Efficient Training on Very Large Corpora via Gramian Estimation, ICLR 2019
- 🧠 Latent Cross: Making Use of Context in Recurrent Recommender Systems, WSDM 2018
- Q&R: A two-stage approach toward interactive recommendation, KDD 2018
- Categorical-Attributes-Based Multi-Level Classification for Recommender Systems, RecSys 2018
- 🧠🎰 Deep Bayesian Bandits Showdown: an Empirical Comparison of Bayesian Deep Networks for Thompson Sampling, ICLR 2018
- 📑 The LambdaLoss Framework for Ranking Metric Optimization, CIKM 2018
- 🕴🔬 Practical Diversified Recommendations on YouTube with Determinantal Point Processes, CIKM 2018
- Recommendations for All: Solving Thousands of Recommendation Problems Daily, ICDE 2018 (also describe user context representation by the actions taken)
- A Generic Coordinate Descent Framework for Learning from Implicit Feedback, WWW 2017
- 🧠 Deep & Cross Network for Ad Click Predictions, 2017
- 🕴 Deep neural networks for YouTube recommendations, RecSys 2016; video
- 🕴🧠 Wide & Deep Learning for Recommender Systems, DLRS 2016 (workshop on deep learning for recommender systems) – used for Google Play
- Q&R: A two-stage approach toward interactive recommendation, KDD 2018 (Google)
- Towards Conversational Recommender Systems, KDD 2016
- 💵 Focusing on the Long-term: It’s Good for Users and Business, KDD 2015
- 💵 Ad Click Prediction: a View from the Trenches, KDD 2013
- 🕴 The YouTube video recommendation system, RecSys 2010
- Google news personalization: scalable online collaborative filtering, WWW 2007
Talks
Blog posts
- Building Large Scale Recommenders using Cloud TPUs, 2022-10-07
- On YouTube's recommendation system, 2021-09-15
- Advances in TF-Ranking, 2021-07-21
- Scholar Recommendations Reloaded! Fresher, More Relevant, Easier, 2021-02-12
- Announcing ScaNN: Efficient Vector Similarity Search, 2020-07-28
- Advanced machine learning helps Play Store users discover personalised apps, 2019-11-18
Software
- Trax: end-to-end DL library with focus on clear code and speed; used for transformer models, successor to tensor2tensor.
- rax, learning-to-rank framework for JAX, paper
- RecSim blog post
- ScaNN (Scalable Nearest Neighbors)
- TensorFlow Recommenders see also TF Recommenders SIG and TF recommender add-ons
- TensorFlow Ranking
External link
- https://abc.xyz/
- Wikipedia article about Alphabet
- GitHub repositories:
- Google Research -- Google Research has a mono-repo with most of their projects.
- DeepMind
- YouTube
