base on # Reference Materials for System Design Interview ## Chapter 1: Introduction and Overview [1] Data warehouse. https://cloud.google.com/learn/what-is-a-data-warehouse. [2] Structured vs. unstructured data. https://signal.onepointltd.com/post/102gjab/machine-learning-libraries-for-tabular-data-problems. [3] Bagging technique in ensemble learning. https://en.wikipedia.org/wiki/Bootstrap_aggregating. [4] Boosting technique in ensemble learning. https://aws.amazon.com/what-is/boosting/. [5] Stacking technique in ensemble learning. https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/. [6] Interpretability in Machine Mearning. https://blog.ml.cmu.edu/2020/08/31/6-interpretability/. [7] Traditional machine learning algorithms. https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/. [8] Sampling strategies. https://www.scribbr.com/methodology/sampling-methods/. [9] Data splitting techniques. https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/. [10] Class-balanced loss. https://arxiv.org/pdf/1901.05555.pdf. [11] Focal loss paper. https://arxiv.org/pdf/1708.02002.pdf. [12] Focal loss. https://medium.com/swlh/focal-loss-an-efficient-way-of-handling-class-imbalance-4855ae1db4cb. [13] Data parallelism. https://www.telesens.co/2017/12/25/understanding-data-parallelism-in-machine-learning/. [14] Model parallelism. https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-intro.html. [15] Cross entropy loss. https://en.wikipedia.org/wiki/Cross_entropy. [16] Mean squared error loss. https://en.wikipedia.org/wiki/Mean_squared_error. [17] Mean absolute error loss. https://en.wikipedia.org/wiki/Mean_absolute_error. [18] Huber loss. https://en.wikipedia.org/wiki/Huber_loss. [19] L1 and l2 regularization. https://www.analyticssteps.com/blogs/l2-and-l1-regularization-machine-learning. [20] Entropy regularization. https://paperswithcode.com/method/entropy-regularization. [21] K-fold cross validation. https://en.wikipedia.org/wiki/Cross-validation_(statistics). [22] Dropout paper. https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf. [23] Overview of optimization algorithm. https://ruder.io/optimizing-gradient-descent/. [24] Stochastic gradient descent. https://en.wikipedia.org/wiki/Stochastic_gradient_descent. [25] AdaGrad optimization algorithm. https://optimization.cbe.cornell.edu/index.php?title=AdaGrad. [26] Momentum optimization algorithm. https://optimization.cbe.cornell.edu/index.php?title=Momentum. [27] RMSProp optimization algorithm. https://optimization.cbe.cornell.edu/index.php?title=RMSProp. [28] ELU activation function. https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#elu. [29] ReLU activation function. https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#relu. [30] Tanh activation function. https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#tanh. [31] Sigmoid activation function. https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#softmax. [32] FID score. https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance. [33] Inception score. https://en.wikipedia.org/wiki/Inception_score. [34] BLEU metrics. https://en.wikipedia.org/wiki/BLEU. [35] METEOR metrics. https://en.wikipedia.org/wiki/METEOR. [36] ROUGE score. https://en.wikipedia.org/wiki/ROUGE_(metric). [37] CIDEr score. https://arxiv.org/pdf/1411.5726.pdf. [38] SPICE score. https://arxiv.org/pdf/1607.08822.pdf. [39] Quantization-aware training. https://pytorch.org/docs/stable/quantization.html. [40] Model compression survey. https://arxiv.org/pdf/1710.09282.pdf. [41] Shadow deployment. https://christophergs.com/machine%20learning/2019/03/30/deploying-machine-learning-applications-in-shadow-mode/. [42] A/B testing. https://en.wikipedia.org/wiki/A/B_testing. [43] Canary release. https://blog.getambassador.io/cloud-native-patterns-canary-release-1cb8f82d371a. --- ## Chapter 2: Visual Search System [1] Visual search at pinterest. https://arxiv.org/pdf/1505.07647.pdf. [2] Visual embeddings for search at Pinterest. https://medium.com/pinterest-engineering/unifying-visual-embeddings-for-visual-search-at-pinterest-74ea7ea103fo. [3] Representation learning. https://en.wikipedia.org/wiki/Feature_learning. [4] ResNet paper. https://arxiv.org/pdf/1512.03385.pdf. [5] Transformer paper. https://arxiv.org/pdf/1706.03762.pdf. [6] Vision Transformer paper. https://arxiv.org/pdf/2010.11929.pdf. [7] SimCLR paper. https://arxiv.org/pdf/2002.05709.pdf. [8] MoCo paper. https://openaccess.thecvf.com/content_CVPR_2020/papers/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.pdf. [9] Contrastive representation learning methods. https://lilianweng.github.io/posts/2019-11-10-self-supervised/. [10] Dot product. https://en.wikipedia.org/wiki/Dot_product. [11] Cosine similarity. https://en.wikipedia.org/wiki/Cosine_similarity. [12] Euclidean distance. https://en.wikipedia.org/wiki/Euclidean_distance. [13] Curse of dimensionality. https://en.wikipedia.org/wiki/Curse_of_dimensionality. [14] Curse of dimensionality issues in ML. https://www.mygreatlearning.com/blog/understanding-curse-of-dimensionality/. [15] Cross-entropy loss. https://en.wikipedia.org/wiki/Cross_entropy. [16] Vector quantization. http://ws.binghamton.edu/fowler/fowler\%20personal\%20page/EE523_files/Ch_10_1\%20VQ\%20Description\%20(PPT).pdf. [17] Product quantization. https://towardsdatascience.com/product-quantization-for-similarity-search-2f1f67c5fddd. [18] R-Trees. https://en.wikipedia.org/wiki/R-tree. [19] KD-Tree. https://kanoki.org/2020/08/05/find-nearest-neighbor-using-kd-tree/. [20] Annoy. https://towardsdatascience.com/comprehensive-guide-to-approximate-nearest-neighbors-algorithms-8b94f057d6b6. [21] Locality-sensitive hashing. https://web.stanford.edu/class/cs246/slides/03-1sh.pdf. [22] Faiss library. https://github.com/facebookresearch/faiss/wiki. [23] ScaNN library. https://github.com/google-research/google-research/tree/master/scann. [24] Content moderation with ML. https://appen.com/blog/content-moderation/. [25] Bias in $\mathrm{AI}$ and recommendation systems. https://www.searchenginejournal.com/biases-search-recommender-systems/339319/\#close. [26] Positional bias. https://eugeneyan.com/writing/position-bias/. [27] Smart crop. https://blog.twitter.com/engineering/en_us/topics/infrastructure/2018/Smart-Auto-Cropping-of-Images. [28] Better search with gnns. https://arxiv.org/pdf/2010.01666.pdf. [29] Active learning. https://en.wikipedia.org/wiki/Active_learning_(machine_learning). [30] Human-in-the-loop ML. https://arxiv.org/pdf/2108.00941.pdf. --- ## Chapter 3: Google Street View Blurring System [1] Google Street View. https://www.google.com/streetview. [2] DETR. https://github.com/facebookresearch/detr. [3] RCNN family. https://lilianweng.github.io/posts/2017-12-31-object-recognition-part-3. [4] Fast R-CNN paper. https://arxiv.org/pdf/1504.08083.pdf. [5] Faster R-CNN paper. https://arxiv.org/pdf/1506.01497.pdf. [6] YOLO family. https://pyimagesearch.com/2022/04/04/introduction-to-the-yolo-family. [7] SSD. https://jonathan-hui.medium.com/ssd-object-detection-single-shot-multibox-detector-for-real-time-processing-9bd8deac0e06. [8] Data augmentation techniques. https://www.kaggle.com/getting-started/190280. [9] CNN. https://en.wikipedia.org/wiki/Convolutional_neural_network. [10] Object detection details. https://dudeperf3ct.github.io/object/detection/2019/01/07/Mystery-of-Object-Detection. [11] Forward pass and backward pass. https://www.youtube.com/watch?v=qzPQ8cEsVK8. [12] MSE. https://en.wikipedia.org/wiki/Mean_squared_error. [13] Log loss. https://en.wikipedia.org/wiki/Cross_entropy. [14] Pascal VOC. http://host.robots.ox.ac.uk/pascal/VOC/voc2008/index.html. [15] COCO dataset evaluation. https://cocodataset.org/\#detection-eval. [16] Object detection evaluation. https://github.com/rafaelpadilla/Object-Detection-Metrics. [17] NMS. https://en.wikipedia.org/wiki/NMS. [18] Pytorch implementation of NMS. https://learnopencv.com/non-maximum-suppression-theory-and-implementation-in-pytorch/. [19] Recent object detection models. https://viso.ai/deep-learning/object-detection/. [20] Distributed training in Tensorflow. https://www.tensorflow.org/guide/distributed_training. [21] Distributed training in Pytorch. https://pytorch.org/tutorials/beginner/dist_overview.html. [22] GDPR and ML. https://www.oreilly.com/radar/how-will-the-gdpr-impact-machine-learning. [23] Bias and fairness in face detection. http://sibgrapi.sid.inpe.br/col/sid.inpe.br/sibgrapi/2021/09.04.19.00/doc/103.pdf. [24] AI fairness. https://www.kaggle.com/code/alexisbcook/ai-fairness. [25] Continual learning. https://towardsdatascience.com/how-to-apply-continual-learning-to-your-machine-learning-models-4754adcd7f7f. [26] Active learning. https://en.wikipedia.org/wiki/Active_learning_(machine_learning). [27] Human-in-the-loop ML. https://arxiv.org/pdf/2108.00941.pdf. --- ## Chapter 4: YouTube Video Search [1] Elasticsearch. https://www.tutorialspoint.com/elasticsearch/elasticsearch_query_dsl.htm. [2] Preprocessing text data. https://huggingface.co/docs/transformers/preprocessing. [3] NFKD normalization. https://unicode.org/reports/tr15/. [4] What is Tokenization summary. https://huggingface.co/docs/transformers/tokenizer_summary. [5] Hash collision. https://en.wikipedia.org/wiki/Hash_collision. [6] Deep learning for NLP. http://cs224d.stanford.edu/lecture_notes/notes1.pdf. [7] TF-IDF. https://en.wikipedia.org/wiki/Tf\%E2\%80\%93idf. [8] Word2Vec models. https://www.tensorflow.org/tutorials/text/word2vec. [9] Continuous bag of words. https://www.kdnuggets.com/2018/04/implementing-deep-learning-methods-feature-engineering-text-data-cbow.html. [10] Skip-gram model. http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/. [11] BERT model. https://arxiv.org/pdf/1810.04805.pdf. [12] GPT3 model. https://arxiv.org/pdf/2005.14165.pdf. [13] BLOOM model. https://bigscience.huggingface.co/blog/bloom. [14] Transformer implementation from scratch. https://peterbloem.nl/blog/transformers. [15] 3D convolutions. https://www.kaggle.com/code/shivamb/3d-convolutions-understanding-use-case/notebook. [16] Vision Transformer. https://arxiv.org/pdf/2010.11929.pdf. [17] Query understanding for search engines. https://www.linkedin.com/pulse/ai-query-understanding-daniel-tunkelang/. [18] Multimodal video representation learning. https://arxiv.org/pdf/2012.04124.pdf. [19] Multilingual language models. https://arxiv.org/pdf/2107.00676.pdf. [20] Near-duplicate video detection. https://arxiv.org/pdf/2005.07356.pdf. [21] Generalizable search relevance. https://livebook.manning.com/book/ai-powered-search/chapter-10/v-10/20. [22] Freshness in search and recommendation systems. https://developers.google.com/machine-learning/recommendation/dnn/re-ranking. [23] Semantic product search by Amazon. https://arxiv.org/pdf/1907.00937.pdf. [24] Ranking relevance in Yahoo search. https://www.kdd.org/kdd2016/papers/files/adf0361-yinA.pdf. [25] Semantic product search in E-Commerce. https://arxiv.org/pdf/2008.08180.pdf. --- ## Chapter 5: Harmful Content Detection [1] Facebook’s inauthentic behavior. https://transparency.fb.com/policies/community-standards/inauthentic-behavior/. [2] LinkedIn’s professional community policies. https://www.linkedin.com/legal/professional-community-policies. [3] Twitter’s civic integrity policy. https://help.twitter.com/en/rules-and-policies/election-integrity-policy. [4] Facebook’s integrity survey. https://arxiv.org/pdf/2009.10311.pdf. [5] Pinterest’s violation detection system. https://medium.com/pinterest-engineering/how-pinterest-fights-misinformation-hate-speech-and-self-harm-content-with-machine-learning-1806b73b40ef. [6] Abusive detection at LinkedIn. https://engineering.linkedin.com/blog/2019/isolation-forest. [7] WPIE method. https://ai.facebook.com/blog/community-standards-report/. [8] BERT paper. https://arxiv.org/pdf/1810.04805.pdf. [9] Multilingual DistilBERT. https://huggingface.co/distilbert-base-multilingual-cased. [10] Multilingual language models. https://arxiv.org/pdf/2107.00676.pdf. [11] CLIP model. https://openai.com/blog/clip/. [12] SimCLR paper. https://arxiv.org/pdf/2002.05709.pdf. [13] VideoMoCo paper. https://arxiv.org/pdf/2103.05905.pdf. [14] Hyperparameter tuning. https://cloud.google.com/ai-platform/training/docs/hyperparameter-tuning-overview. [15] Overfitting. https://en.wikipedia.org/wiki/Overfitting. [16] Focal loss. https://amaarora.github.io/2020/06/29/FocalLoss.html. [17] Gradient blending in multimodal systems. https://arxiv.org/pdf/1905.12681.pdf. [18] ROC curve vs precision-recall curve. https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/. [19] Introduced bias by human labeling. https://labelyourdata.com/articles/bias-in-machine-learning. [20] Facebook’s approach to quickly tackling trending harmful content. https://ai.facebook.com/blog/harmful-content-can-evolve-quickly-our-new-ai-system-adapts-to-tackle-it/. [21] Facebook’s TIES approach. https://arxiv.org/pdf/2002.07917.pdf. [22] Temporal interaction embedding. https://www.facebook.com/atscaleevents/videos/730968530723238/. [23] Building and scaling human review system. https://www.facebook.com/atscaleevents/videos/1201751883328695/. [24] Abusive account detection framework. https://www.youtube.com/watch?v=YeX4MdU0JNk. [25] Borderline contents. https://transparency.fb.com/features/approach-to-ranking/content-distribution-guidelines/content-borderline-to-the-community-standards/. [26] Efficient harmful content detection. https://about.fb.com/news/2021/12/metas-new-ai-system-tackles-harmful-content/. [27] Linear Transformer paper. https://arxiv.org/pdf/2006.04768.pdf. [28] Efficient AI models to detect hate speech. https://ai.facebook.com/blog/how-facebook-uses-super-efficient-ai-models-to-detect-hate-speech/. --- ## Chapter 6: Video Recommendation System [1] YouTube recommendation system. https://blog.youtube/inside-youtube/on-youtubes-recommendation-system. [2] DNN for YouTube recommendation. https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf. [3] CBOW paper. https://arxiv.org/pdf/1301.3781.pdf. [4] BERT paper. https://arxiv.org/pdf/1810.04805.pdf. [5] Matrix factorization. https://developers.google.com/machine-learning/recommendation/collaborative/matrix. [6] Stochastic gradient descent. https://en.wikipedia.org/wiki/Stochastic_gradient_descent. [7] WALS optimization. https://fairyonice.github.io/Learn-about-collaborative-filtering-and-weighted-alternating-least-square-with-tensorflow.html. [8] Instagram multi-stage recommendation system. https://ai.facebook.com/blog/powered-by-ai-instagrams-explore-recommender-system/. [9] Exploration and exploitation trade-offs. https://en.wikipedia.org/wiki/Multi-armed_bandit. [10] Bias in AI and recommendation systems. https://www.searchenginejournal.com/biases-search-recommender-systems/339319/#close. [11] Ethical concerns in recommendation systems. https://link.springer.com/article/10.1007/s00146-020-00950-y. [12] Seasonality in recommendation systems. https://www.computer.org/csdl/proceedings-article/big-data/2019/09005954/1hJsfgT0qL6. [13] A multitask ranking system. https://daiwk.github.io/assets/youtube-multitask.pdf. [14] Benefit from a negative feedback. https://arxiv.org/abs/1607.04228?context=cs. --- ## Chapter 7: Event Recommendation System [1] Learning to rank methods. https://livebook.manning.com/book/practical-recommender-systems/chapter-13/53. [2] RankNet paper. https://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf. [3] LambdaRank paper. https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/lambdarank.pdf. [4] LambdaMART paper. https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR-TR-2010-82.pdf. [5] SoftRank paper. https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/SoftRankWsdm08Submitted.pdf. [6] ListNet paper. https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-2007-40.pdf. [7] AdaRank paper. https://dl.acm.org/doi/10.1145/1277741.1277809. [8] Batch processing vs stream processing. https://www.confluent.io/learn/batch-vs-real-time-data-processing/#:~:text=Batch%20processing%20is%20when%20the,data%20flows%20through%20a%20system. [9] Leveraging location data in ML systems. https://towardsdatascience.com/leveraging-geolocation-data-for-machine-learning-essential-techniques-192ce3a969bc#:~:text=Location%20data%20is%20an%20important,based%20on%20your%20customer%20data. [10] Logistic regression. https://www.youtube.com/watch?v=yIYKR4sgzI8. [11] Decision tree. https://careerfoundry.com/en/blog/data-analytics/what-is-a-decision-tree/. [12] Random forests. https://en.wikipedia.org/wiki/Random_forest. [13] Bias/variance trade-off. http://www.cs.cornell.edu/courses/cs578/2005fa/CS578.bagging.boosting.lecture.pdf. [14] AdaBoost. https://en.wikipedia.org/wiki/AdaBoost. [15] XGBoost. https://xgboost.readthedocs.io/en/stable/. [16] Gradient boosting. https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/. [17] XGBoost in Kaggle competitions. https://www.kaggle.com/getting-started/145362. [18] GBDT. https://blog.paperspace.com/gradient-boosting-for-classification/ [19] An introduction to GBDT. https://www.machinelearningplus.com/machine-learning/an-introduction-to-gradient-boosting-decision-trees/. [20] Introduction to neural networks. https://www.youtube.com/watch?v=0twSSFZN9Mc. [21] Bias issues and solutions in recommendation systems. https://www.youtube.com/watch?v=pPq9iyGIZZ8. [22] Feature crossing to encode non-linearity. https://developers.google.com/machine-learning/crash-course/feature-crosses/encoding-nonlinearity. [23] Freshness and diversity in recommendation systems. https://developers.google.com/machine-learning/recommendation/dnn/re-ranking. [24] Privacy and security in ML. https://www.microsoft.com/en-us/research/blog/privacy-preserving-machine-learning-maintaining-confidentiality-and-preserving-trust/. [25] Two-sides marketplace unique challenges. https://www.uber.com/blog/uber-eats-recommending-marketplace/. [26] Data leakage. https://machinelearningmastery.com/data-leakage-machine-learning/. [27] Online training frequency. https://huyenchip.com/2022/01/02/real-time-machine-learning-challenges-and-solutions.html#towards-continual-learning. --- ## Chapter 8: Ad Click Prediction on Social Platforms [1] Addressing delayed feedback. https://arxiv.org/pdf/1907.06558.pdf. [2] AdTech basics. https://advertising.amazon.com/library/guides/what-is-adtech. [3] SimCLR paper. https://arxiv.org/pdf/2002.05709.pdf. [4] Feature crossing. https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture. [5] Feature extraction with GBDT. https://towardsdatascience.com/feature-generation-with-gradient-boosted-decision-trees-21d4946d6ab5. [6] DCN paper. https://arxiv.org/pdf/1708.05123.pdf. [7] DCN V2 paper. https://arxiv.org/pdf/2008.13535.pdf. [8] Microsoft’s deep crossing network paper. https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf. [9] Factorization Machines. https://www.jefkine.com/recsys/2017/03/27/factorization-machines/. [10] Deep Factorization Machines. https://d2l.ai/chapter_recommender-systems/deepfm.html. [11] Kaggle’s winning solution in ad click prediction. https://www.youtube.com/watch?v=4Go5crRVyuU. [12] Data leakage in ML systems. https://machinelearningmastery.com/data-leakage-machine-learning/. [13] Time-based dataset splitting. https://www.linkedin.com/pulse/time-based-splitting-determining-train-test-data-come-manraj-chalokia/?trk=public_profile_article_view. [14] Model calibration. https://machinelearningmastery.com/calibrated-classification-model-in-scikit-learn/. [15] Field-aware Factorization Machines. https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf. [16] Catastrophic forgetting problem in continual learning. https://www.cs.uic.edu/~liub/lifelong-learning/continual-learning.pdf. --- ## Chapter 9: Similar Listings on Vacation Rental Platforms [1] Instagram’s Explore recommender system. https://ai.facebook.com/blog/powered-by-ai-instagrams-explore-recommender-system. [2] Listing embeddings in search ranking. https://medium.com/airbnb-engineering/listing-embeddings-for-similar-listing-recommendations-and-real-time-personalization-in-search-601172f7603e. [3] Word2vec. https://en.wikipedia.org/wiki/Word2vec. [4] Negative sampling technique. https://www.baeldung.com/cs/nlps-word2vec-negative-sampling. [5] Positional bias. https://eugeneyan.com/writing/position-bias/. [6] Random walk. https://en.wikipedia.org/wiki/Random_walk. [7] Random walk with restarts. https://www.youtube.com/watch?v=HbzQzUaJ_9I. [8] Seasonality in recommendation systems. https://www.computer.org/csdl/proceedings-article/big-data/2019/09005954/1hJsfgT0qL6. --- ## Chapter 10: Personalized News Feed [1] News Feed ranking in Facebook. https://engineering.fb.com/2021/01/26/ml-applications/news-feed-ranking/. [2] Twitter’s news feed system. https://blog.twitter.com/engineering/en_us/topics/insights/2017/using-deep-learning-at-scale-in-twitters-timelines. [3] LinkedIn’s News Feed system LinkedIn. https://engineering.linkedin.com/blog/2020/understanding-feed-dwell-time. [4] BERT paper. https://arxiv.org/pdf/1810.04805.pdf. [5] ResNet model. https://arxiv.org/pdf/1512.03385.pdf. [6] CLIP model. https://openai.com/blog/clip/. [7] Viterbi algorithm. https://en.wikipedia.org/wiki/Viterbi_algorithm. [8] TF-IDF. https://en.wikipedia.org/wiki/Tf%E2%80%93idf. [9] Word2vec. https://en.wikipedia.org/wiki/Word2vec. [10] Serving a billion personalized news feed. https://www.youtube.com/watch?v=Xpx5RYNTQvg. [11] Mean absolute error loss. https://en.wikipedia.org/wiki/Mean_absolute_error. [12] Means squared error loss. https://en.wikipedia.org/wiki/Mean_squared_error. [13] Huber loss. https://en.wikipedia.org/wiki/Huber_loss. [14] A news feed system design. https://liuzhenglaichn.gitbook.io/system-design/news-feed/design-a-news-feed-system. [15] Predict viral tweets. https://towardsdatascience.com/using-data-science-to-predict-viral-tweets-615b0acc2e1e. [16] Cold start problem in recommendation systems. https://en.wikipedia.org/wiki/Cold_start_(recommender_systems). [17] Positional bias. https://eugeneyan.com/writing/position-bias/. [18] Determine retraining frequency. https://huyenchip.com/2022/01/02/real-time-machine-learning-challenges-and-solutions.html#towards-continual-learning. --- ## Chapter 11: People You May Know [1] Clustering in ML. https://developers.google.com/machine-learning/clustering/overview. [2] PYMK on Facebook. https://youtu.be/Xpx5RYNTQvg?t=1823. [3] Graph convolutional neural networks. http://tkipf.github.io/graph-convolutional-networks/. [4] GraphSage paper. https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf. [5] Graph attention networks. https://arxiv.org/pdf/1710.10903.pdf. [6] Graph isomorphism network. https://arxiv.org/pdf/1810.00826.pdf. [7] Graph neural networks. https://distill.pub/2021/gnn-intro/. [8] Personalized random walk. https://www.youtube.com/watch?v=HbzQzUaJ_9I. [9] LinkedIn’s PYMK system. https://engineering.linkedin.com/blog/2021/optimizing-pymk-for-equity-in-network-creation. [10] Addressing delayed feedback. https://arxiv.org/pdf/1907.06558.pdf ", Assign "at most 3 tags" to the expected json: {"id":"3971","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"