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We're running a special series on recommendation technologies and in this post we look at the different approaches - including a look at how Amazon and Google use recommendations.
Information providers are a very promising application area of recommender systems due to the general problem of assessing the quality of information products prior to the purchase. Recommender systems automatically generate product recommendations: customers profit from a faster finding of relevant products, stores profit from rising sales. All aspects of recommender systems are covered: the economic background, mechanism design, a survey of systems in the Internet, statistical methods and algorithms, service oriented architectures, user interfaces, as well as experiences and data from real-world applications.
Recommender systems suggest information sources and products to users based on learning from examples of their likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences.
Abstract Recommender systems are useful tools for adding a reference component to a library catalog, and they help develop library catalogs that serve as customer-oriented portals, deploying Web 2.0 technology. Recommender systems are based on statistical models, and they can lead users from one record to similar literature held in the catalog. In this article we describe the recommender system BibTip, developed in Karlsruhe University, and we discuss its application in libraries. Recommender Systems in General
Earlier this week we posted a Guide to Recommender Systems , as part of our series on recommendation technologies . In this post we look at some of the challenges in building or deploying a recommender system. And yes, Napoleon Dynamite is one of them.
By Alex Iskold
Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that user would give to an item (such as music , books , or movies ) or social element (e.g. people or groups ) they had not yet considered, using a model built from the characteristics of an item (content-based approaches) or the user's social environment (collaborative filtering approaches). [ 1 ] [ 2 ] Recommender systems have become extremely common in recent years. A few examples of such systems:
This podcasting tutorial will show you how to create and publish your very own podcast quickly and easily! Think about listening to a radio show on a topic that you're interested in, but instead of having to tune in at a specific time and station, you can listen to the show at the time and place of your choosing. That's what podcasting enables you to do.
This podcast tutorial is broken down into four steps: Plan Produce Publish Promote Let's take a quick look at each section of the podcast tutorial.
In this Audacity tutorial you'll finally press record.
Audacity and Windows 7 Current versions of Audacity fully support Windows 7.