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Netflix Application Case

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Q1 Usefulness of recommender Engines

Customers often face the challenge of choosing what to buy or rent, especially when they have a large inventory to choose from. For instance, customers at Netflix had a hard time determining the shows to watch since they had to choose from several million titles. The primary role of recommendation engines was to enhance customer satisfaction by recommending shows tailored to their tastes. In addition, the recommender systems are useful in gaining a competitive advantage over competitors. Netflix faced stiff competition from Amazon, Google operator, and Apple. Using recommendation engines is a perfect strategy to distinguish a firm from the competition by making useful recommendations to its customers. The number of Netflix subscribers has grown exponentially from 10 million in 2008 to 118 million in 2018.

Q2 How recommendation is generated

Traditionally, recommender engines worked by analyzing the experience of a user. For instance, at Netflix, the recommendation engines accomplished their task by comparing individual likes and ratings. The recommender engine uses this data to suggest a similar product to the customer. These systems had their shortcomings and were not very effective in providing effective recommendations. Modern-day recommender engines use Artificial intelligence and Machine learning Algorithms to analyze big data and draw insights to generate recommendations. These recommender engines use technologies such as content filtering and collaborative filtering to determine the consumers’ preference for products and services.

Q3 Reasons why Netflix did not Disclose its recommendation Algorithms

Unlike Amazon’s recommendation engines, the recommendation engines at Netflix were based on what other people in the same country enjoyed. Despite the effectiveness of this technology, the recommender systems did not work well in the global markets. The main reason that attributed this drawback was the difference in culture, social practices, and political environments. This drawback was the primary reason why Netflix could not disclose its recommendation engines. As a result, the implementation of the Netflix recommendation engine was difficult, especially when a new country was added.

Q4 The research activities that attempt to “mimic  the human brain.”

The new era of recommender systems leads to the development of recommender engines that used Artificial Intelligence and machine learning algorithms to determine what the consumers want. These algorithms work by analyzing rate products and recommend similar products. These recommender engines use information such as user-user relationships, product=product algorithms, and user-product relationships to make predictions. For instance, collaborative filtering recommender systems use product=product relationships to make predication. For example, if a user is interested in football shows, the engine will suggest football-related shows to the consumer.

Q5 changes due to globalization

Globalization defines the expansion of a firm’s operations and functionality beyond national borders and cultures. Netflix globalization increased its competition levels as well as the challenges of adapting to a different culture, social behaviors, and political environment. in order to adapt to the different culture effectively, Netflix had to revise its recommendation engines and set them to personalize the needs and requirements of each country. In addition, the recommendation engines had to implement modern technologies such as cloud computing and big data analysis to compete with other retail giants such as Amazon.

 

 

 

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