Accounting for Taste

Pescovitz, David

doi:10.1038/scientificamerican0600-44

Cited by 9 publications

(2 citation statements)

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“…Aggregation is an important part of the function of a recommender system because the quality of the provided recommendation is much dependent on the way the collabora-tive filtering algorithm calculates the similarity/dissimilarity of the user's profile with the data already gathered. However, user interaction is something that relies on both explicit and implicit data (not directly obtained) and may contain noise (Pescovitz, 2000). The above constitutes our discussion agenda for the design of recommendation systems with emphasis to microcontent.…”

Section: Recommender Systems For Content and Microcontentmentioning

confidence: 99%

Microlearning in health professions education: a scoping review protocol

Gagné

Woodward

Park

et al. 2019

JBI Database of Systematic Reviews and Implementation Reports

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MicroContent is a new phase in the development of Internet. After sites and web-pages, MicroContent defines an even smaller content fragment. MicroContent Items are content fragments that focused, self-contained, indivisible, structured and addressable. These attributes allow a new phenomenon on Internet: mixing and mashing. There are multiple ways how MicroContent can be combined into larger macrocontent containers, such as web-pages, applications, DVD's, etc. Although we are at the very early stages of this development several mashing parameters can be described.MicroContent mashing allows the audience to become the producer and will have impact on many content usages, such as learning.

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Section: Recommender Systems For Content and Microcontentmentioning

confidence: 99%

Microlearning in health professions education: a scoping review protocol

Gagné

Woodward

Park

et al. 2019

JBI Database of Systematic Reviews and Implementation Reports

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show abstract

“…Firms will likely use discrete-choice modeling to estimate market share and product demand [Montgomery 2001]. Firms, such as Amazon.com, are using collaborative filtering and similar techniques to personalize web-site content by predicting individual preferences based on the navigational paths of customers considered to have similar tastes [Pescovitz 2000].…”

Section: Decision Support In Airline Planning and Marketingmentioning

confidence: 99%

E-Commerce and Operations Research in Airline Planning, Marketing, and Distribution

et al. 2001

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Many e-commerce principles were pioneered in the airline industry. These include the first business-to-business electronic information exchange and industry-wide electronic marketplace. This environment provided unprecedented opportunity for operations research (OR) modeling. By the mid-1980s airlines used customer shopping data to calibrate traveler demand and choice models, analyzed multi-channel product distribution strategies with simulation and practiced dynamic pricing through yield management.Airlines continue to derive billions of dollars annually from these and derivative models. The availability of reliable, low-cost communications via the Internet is not only providing new modeling challenges within the airline industry, but it is also providing similar opportunities in other industries.

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Commercial Tools for the Development of Personalized Web Applications: A Survey

Maurino

Fraternali

2002

E-Commerce and Web Technologies

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Accounting for Taste

Cited by 9 publications

References 0 publications

Microlearning in health professions education: a scoping review protocol

Microlearning in health professions education: a scoping review protocol

E-Commerce and Operations Research in Airline Planning, Marketing, and Distribution

Commercial Tools for the Development of Personalized Web Applications: A Survey

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