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Blog - Page 21

Over the past 25 years, unicorns have grown fast. Nevertheless, tech stars still do not have a path to high profits. Superstar firms have proved that wealth is made by huge markets, high profits and natural monopolies.

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Content analysis and data mining of user tags are the cornerstones of the recommendation system. The content analysis derives intermedia data from raw articles and user behaviors. With content analysis, we are able to tag users, recommend and prepare content.

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Motivation and incentives are at the core of performance management. Employee motivation primarily comes from two aspects: extrinsic and intrinsic incentives. Extrinsic rewards (monetary rewards) have a short-term effect and may not necessarily improve performance; fairness and consistency are also essential. Intrinsic rewards vary by individual and can be achieved through the following three methods: recognizing employees' work, providing decision-making authority, and offering challenges.

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In order to evaluate user satisfaction, machine learning models are implemented. These models observe and measure the reality by feature engineering and further reduce latencies by recall strategy.

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What are we optimizing for? User Satisfaction

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Stream and Batch processing frameworks can process high throughput at low latency. Why is Flink gaining popularity? And how to make an architectural choice among Storm, Storm-trident, Spark, and Flink?

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Fraud Detection fights against account takeovers and Botnet attacks during login. Semi-supervised learning has better learning accuracy than unsupervised learning and less time and costs than supervised learning.

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Requirements for designing Uber ride-hailing: providing services for the global transportation market; large-scale real-time scheduling; backend design; Uber ride-hailing design process: architecture; microservices; scheduling services; payment services; user profile services and trip record services, notification push services.

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