Recommendation websites stumbleupon and pandora radio among many others
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Recommendation Websites StumbleUpon and Pandora Radio among many others. How personalized can a recommendation website be? . What is the recommendation website/search engines?.

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Recommendation Websites StumbleUpon and Pandora Radio among many others.

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Recommendation websites stumbleupon and pandora radio among many others

Recommendation WebsitesStumbleUpon and Pandora Radio among many others.

How personalized can a recommendation website be?

What is the recommendation website search engines

What is the recommendation website/search engines?

  • Recommendation websites attempt to recommend media(music, art, video), websites, and other types of information based on a users taste.

  • There are many methods with which the recommender website tries to do this known as recommender systems. There also many different websites that use these recommender systems for different purposes.

Recommendation websites stumbleupon and pandora radio among many others

  • Among these websites is StumbleUpon and Pandora Radio.

  • As said before there are a number of other websites making use of recommender systems but I choose these in particular so we can get a good cross section of the typical use of recommender systems for specialized recommender websites.

  • I choose StumbleUpon because it is the most popular one and Pandora because of the creation of the “Music Genome Project”, it covers the whole idea of recommender systems that attempts to analyze a users taste in something as subjective as art.

The recommender system and the recommender website

The recommender System and The Recommender website.

  • Recommender Systems are different from recommendation websites in that these systems may be present on websites whose sole purpose is not just to recommend. For example the recommender system on informs the user of items purchased by other users who also bought said items. The “did you mean” feature of Google can help you articulate your search better.

Brief outline of how recommender systems work

Brief outline of how recommender systems work.

  • System collects information about the user in two ways:-

    • Implicit data collection: is a noninvasive method where the computer analyzes the items bought and the viewing times of specific items, Recording what the user purchased online. Lastly analyzing the user’s social network to see what similarities they may have in terms of likes and dislikes.

    • Explicit data collection: This is a form of collection that requests the user to input information about themselves. Requesting users to rank or rate items, asking the user to create a list of items they like or asking them to choose between two items.

  • The information collected about the user is compared it information stored about other users. The system then suggests items to the user that other users with similar tastes enjoyed.

Recommender systems and their presence on the internet

Recommender Systems and their presence on the internet.

  • Recommender System are a such a major part of the internet today that there very few successful websites that don’t have some sort of recommender system at use.

  • The search for the best recommender system is still on going as revisions of the algorithms behind these systems still appear today. The Netflix Prize awarded a million dollars to the grand prize winner of their competition to find the best collaborative filtering algorithm.

  • Recommender systems may seem controversial to some especially when joined with social networking. There are some who are uncomfortable with how much access the system has to their personal information and how it shares this information. However this is not a major problem because the systems are discreet and don’t give away important information about users.



“We created StumbleUpon so people can “stumble upon” sites that have been submitted and rated by like-minded people, rather than presented with the most popular sites for a given keyword.”

Garret Camp in an interview with Chris Sherman of

A brief history of stumbleupon

A Brief History of StumbleUpon

  • The website was founded in 2001 by:

    Garret Camp Geoff Smith Justin LeFrance

    • And Eric Boyd.

      *the website was founded while Garret Camp was doing his postgraduate degree at the University of Calgary in Alberta Canada.

Recommendation websites stumbleupon and pandora radio among many others

  • StumbleUpon’s fast popularity attracted the likes of Brad O’Neil, Ram Shriram, a billionaire who was one of the founding board members of Google, and Mitch Kapor of Mozilla Firefox. Collectively they fundraised $1.2 million US dollars.

  • In 2007 eBay bought StumbleUpon for $75,000,000 US dollars

  • In 2009 Camp, and Smith among many other investors where able to buy the company back for what is rumored to be less than what the people at EBay bought it for.

Recommendation websites stumbleupon and pandora radio among many others


  • StumbleUpon uses a recommender system known as “collaborative filtering”.

  • StumbleUpon also has a social networking aspect as the users, known as stumblers, are able to comment on the websites recommended to them. They also critic each others blogs. People who seem to have similar preferences based on the information collected by StumbleUpon form social networks.

Recommendation websites stumbleupon and pandora radio among many others

  • The people at stumble Upon make profit in two ways through sponsors in which stumblers pay a certain “donation”( a suggestion of 20 dollars a year). This donation gives them privileges other stumblers don’t have. Some sites are sponsored, but around only 2% of StumbleUpon sites fall under this category.

  • Ways to Stumble:-

    • Classic stumble. User ticks of their interests and they are shown a website they click stumble to move on and as they go on they can give websites a thumbs up or down and this helps the system narrow down their tastes to better the stumbling.

    • Stumble Thru. The user is able to stumble through particular websites instead of through different web pages from different sites.

    • Stumble Video and Stumble video is a website where users without the toolbar stumble through videos submitted by people from the StumbleUpon website who have helps user shorten URLs to post on their website eg. Facebook statues.

  • Recommendation websites stumbleupon and pandora radio among many others

    • StumbleUpon is a very addictive website. Not only does it entail surfing the internet where the content is specifically geared towards your personal taste it also has a social networking aspect.

    • Although it hasn’t become a major concern to many. Stumbleupon among other social networking websites is a source of procrastination for many, which may seem trivial but is dangerous.

    Pandora radio music genome project

    Pandora Radio./Music Genome Project.

    “I’ve always believed the idea was a good one—the basic idea of profiling a user’s tastes, capturing it in the Genome, and creating a technology that helps people discover music.”

    -Tim Westergren founder Pandora Media inc and Chief Strategy Officer of Pandora Inc. in an interview with Sanpshot Music and Art Foundation.

    Meet the creators

    Meet the Creators!

    Will Glaser Tim Westergren Jon Kraft Nolan Gasser

    *Nolan Glasser began playing the piano at the age of two and composed at 8!He also studied for two years in Paris.

    Pandora through the years

    Pandora through the years.

    • Will Glaser and Tim Westergren are the brains behind Pandora Radio(1999) who joined with Jon Kraft to create Pandora Media Inc. (2000).Currently ,the Music Genome Project, a very complicated form of recommender system, is owned by Pandora Media Inc.

    • In 2007 Sound Exchange's request that internet radio pay double the price in royalties per song than satellite radio was met. Continuous disagreements abut royalties led to all American Internet Radio to be restricted to play in only America. This also lead to users being restricted to 40 hours of free listening per month and having to pay .99 cents should they exceed their listening hours.

    • In 2008 Pandora Radio launched a mobile version of the service through the iTunes App store and the app is usable for the iPod iPhone and iPad. The app is also available for Android phones and The BlackBerry among others.

    Recommendation websites stumbleupon and pandora radio among many others

    • How it works:-

      • Pandora Radio was developed using the Music Genome Project. To the user it may seem as simple as the they input a song they enjoy and get back similar song, but behind this is a very complicated recommender system.

      • The Music Genome Project is as personalized as “automated music recommendation” can be. Specialized musicians working at the company analyze each song categorizing them using up to or more than 400 attributes! This Analysis take up to 20 minutes depending on the song. Pandora claims they have around 800,000 and add thousands every month. The attributes range from tonality and ostinato to categories named “knack for Cathy tunes” or “Intelligent Dance Music”

    How personalized is pandora

    How personalized is Pandora?

    • Although Pandora approached their recommendation service in a way that makes it seems more personalized it also the service has it’s faults. The service relies on the opinion of well qualified musicians. It is impossible for them not to be subjective, however minor the extent of this subjectveness. How educated are they on what real gangster rap is?

    • In addition, there is also the question, am I really discovering new music if I listen to music that is only my taste? Some may argue that you can’t discover what you already love and discovery should be left to experience.

    • Can something as artistic and subjective as music be put through a scientific procedure and categorized?



    • All in all we can say this about recommender systems, they are a very apparent part of the online world today. For the question of how personalized they can get we can conclude from the numbers they draw they do work and are as close as one can get to machine learning in terms of machines learning about taste. Continuous revision of current algorithms used in these systems is sure to lead to something beyond our imagination.

    Sites behind the research

    Sites behind the research.






    • Http://






    The end

    THE END!

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