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The changes in privacy laws will prohibit companies from using consumers’ information without their consent, thus protecting the privacy rights of all consumers (Education Week, 2015).
Netflixs "recommender system" is a software program that is designed to learn about users by gathering data about them and drawing conclusions. “Machine learning” employs Artificial intelligence to recognize the needs of customers that uses the services of a particular company (Newitz, 2009). Computers can now supply the information that was used to be provided by individuals. Artificial Intelligence is the pillar behind Maps, Siri, and Passbook; gadgets are now able to do great things without help from users (Jackson, 2012).
Rather than requesting users to rate products on a five-star scale, Netflix’s recommendation systems need to ask them to evaluate products in pairs; it will offer a correct representation of preferences of consumers. Besides recommending movies, Netflix needs to diversify and support music and games. Music and games are lucrative areas that will benefit the company. Users should be afforded an opportunity to offer feedback on how to improve the recommendation system. Netflix can offer a platform where users can give feedback on the effectiveness of the system. The three changes must be included in the system as part of the platform.
Jackson, E. (2012). Whats Going To Be Apples Next Killer Product? Not a New iPhone- Artificial Intelligence. Retrieved on January 6, 2015 from http://www.forbes.com/sites/ericjackson/2012/10/09/whats-going-to-be-apples-next- killer-product-not-a-new-iphone-artificial-intelligence/
Newitz, A. (2009). Annalee Newitz-Artificial Intelligence and the Netflix Prize. Retrieved on January 6, 2015 from
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Other than delivering patient keeper applications, the data repository also allows a configuration that offers business continuity solutions for the downtime support. Patient Keeper Business Continuity This system helps in a situation that the core information systems go down either planned or unplanned.
This paper will outline how this method of valuing an asset fits to be a factor pricing model. This will entail discussing the assumptions relating to the form of stochastic discount model as well as how the factor method is related to the acquiring of equilibrium risk premium.
Without a doubt, these data and information are believed to be the most important asset for business organizations for the reason that they make use of these data to drive useful patterns on the basis of which they take effective decisions. At the present, because of latest tools and technologies such as the Internet this world has turned into the information based age.
The CAPM model therefore relies on the ability to measure market volatility as a whole. With several possible investments available in the market, the model assumes that one can accurately assess the volatility of each of these investments. This is impossible.
Theory of Portfolio Choice offers a basis for understanding relationships between economic variables. According to the theory, “demand for assets is positively related to wealth,” derivable utility from the asset relative to other commodities, and relative liquidity, but inversely related to risks in other commodities (Mishkin 560).
Whilst some of these data may be due to data-snooping from the analytical work of armchair researchers attempting to discern some meaning to what could just be a simple collection of facts, much of the empirical data such as beta, stock volatility, co-variances, etc.
The asset market is primarily made up of stocks and real estate. The demand and supply of stocks or real estate influences asset prices, with provisions of regulation and price control in cases where the government intervenes as need be (Rapp, 2009). Asset prices
The Maryland College and career- ready only standards are of a varied assortment. This range from career in English language and arts, college careers related to mathematics and mathematical operations, literacy in history and
The ultimate consequence was the loss of huge information totalling to around 1.6 million of its patients, vendors, guarantors and employees who have ever worked at the company from the year 1994 to 2004 at its various facilities in Pennsylvania, Delaware, New Jersey and Florida.
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