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Beer ReferenceBeer encyclopedia, Beer styles guide, A guide to rating beer, A sample beer library, Beer glassware guide, Your beer tasting palate, Beer and cheese, RateBeer Glossary, Mail order beer, More... Brewer Resources Improve beer sales, increase consumer education with point of sale promotions. Order professional quality beer bottle neckers, or make your own shelf tags. More Beer Retailer Resources Improve beer sales, increase consumer education with free shelf tags and more. Beer Ratings - Our 100-point scale beer ratings New beers, Beer search, RateBeer Best, Browse by brewer, By country, Last 90 days, Latest ratings, About our ratings, More...

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Popularity Slider: Diving into the long ta « Matt Locke on folksonomies | Main | Vimeo - tagged video » March 1, 2005 Popularity Slider: Diving into the long tail Posted by Seb Paquet The general idea of a recommender system is that it asks for a few examples of things you like and then gives you more things it thinks you might like, based on its knowledge of other people’s preferences. One problem you can often run into when using a recommender system is a bias towards popular items, which are not really that close to what you like but have the favor of many users because of their high visibility.

The best beers by style category for the year 2015 RateBeer Home > RateBeer Best > Top Beers In The World > For the year 2015 JAN 30, 2016 - The following beers are awarded for their outstanding performance within a beer style category or super style category within which is a grouping of similar or related beer styles. The whole set of reviews, millions, along with a particular emphasis on performance in the last year, was used to determine our winners. We recognize the hard work and consistency that goes into making these styles traditionally, and in new brewing traditions, fine appreciation and precision. This is truly all about showcasing an array of the world's finest brewers.

Automated Collaborative Filtering and Semantic Transports - draf This essay focuses on the conceptualization of the issues, comparisons of current technological developments to other historical/evolutionary processes, future of automated collaboration and its implications for economic and social development of the world, and suggestions of what we may want to pursue and avoid. Explanations of the workings of the technology and analysis of the current market are not my purpose here, although some explanations and examples may be appropriate. Please send your suggestions to sasha1@netcom.com

Problems of Personalization Problems of Personalization Often in discussions on search engines, personalization is hailed as the future to come (see the recent Search is a Platform. Where is it Going? between a panel of experts). And some search engines have tried to introduce certain personalization features, like Google with their Personalized search (Google is also doing some very lax personalization by using geolocation to present localized versions of their search engine). Now I’m not talking about being able to switch the site’s layout (MSN offers this), or offering a search history (like A9), because these do not change your result pages.

Best Of Blog Here's an interesting article about something that happened this past weekend. It seems like a simple and pretty good concept: Organize and film a game jam to give folks a view into the ups and downs of indie game development. If you don't know what a game jam is, it could perhaps be summed up as an event in which game developers gather (often in one physical location, but not necessarily) and design and create a game in a short period of time (usually between 24-48 hours (a weekend) to 7 days (a full week)), often based on a theme or idea. They're mostly a non-competitive, fun, coding challenge almost like DonationCoder's own NANY, except done over a week(end). It's a great outlet for creativity and experimentation, and the short time limit liberates you from worrying about it being an utter failure or total crap.

Collaborative filtering - Wikipedia, the f This image shows an example of predicting of the user's rating using collaborative filtering. At first, people rate different items (like videos, images, games). After that, the system is making predictions about user's rating for an item, which the user hasn't rated yet. These predictions are built upon the existing ratings of other users, who have similar ratings with the active user. Peer Review : Web Focus : Nature December 2006 The peer review trial described below has now closed. Nature's analysis of the results is published within the peer-review debate focus.

Google Recommenda Google has created a new module for the personalized homepage that shows you recommendations, based on your search history, your location and on the search history of similar users. The module called "Interesting things for you" features searches, web pages, and gadgets. The searches were previously available in Search History Trends and included the top gaining queries related to your searches. So the recommended items have two qualities: they are popular and related to your searches. Collaborative Filtering Resources Generally, collaborative filtering (CF) is any algorithm that filters information for a user based on a collection of user profiles. Users having similar profiles may share similar interests. For a user, information can be filtered in/out regarding to the behaviors of his or her similar users.

How beer came to medieval England - Medievalists.net Beer is the most popular alcoholic drink in the United Kingdom. It might be a surprise to some readers to know that this beverage was only introduced to England in the latter half of the fourteenth century, and that it arrived thanks to Dutch immigrants. The story of beer in medieval England is told by Milan Pajic in his recent article “‘Ale for an Englishman is a natural drink’: the Dutch and the origins of beer brewing in late medieval England.”

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