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14 Mar 08

New Sales Program Pays Facebook Members - New York Times

www.nytimes.com/...cebook-Friendly-Marketing.html - Preview

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a new application aims to inject more commerce into the social playground by paying Facebook members who help merchants sell to their friends.
All the inventory, order processing and delivery arrangements are handled by the merchants -- just as they would be for any other sale.
3 more annotations...
- Facebook won't receive a cut of the sales made through Market Lodge
- paying Facebook members to drum up sales could be more effective than advertising, which can cost up to $15 for every $100 in sales.
- Pushman said she will face a new problem: figuring out how to persuade more Facebook members to recommend her products in their personal stores.

13 Feb 08

married minors and the future of facebook » mccamon.org

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量化用户信息，还原用户性格——豆瓣，回头是岸！ - 自投罗网|鲁公子的IT咖啡馆 - DonewsBlog

blog.donews.com/...1252416.aspx - Preview

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07 Oct 07

A Way to Find Your Corner of the Internet Sky - New York Times

www.nytimes.com/...07stream.html - Preview

recommendation search stumbleupon whatis

Here is how StumbleUpon works
StumbleUpon has about 12 million sites in its database
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- StumbleUpon borrows from two ideas that are familiar to millions of Web users. One is collaborative filtering
- The other is social networks
- push search
- Web discovery is catching on
- That suggests that push search and discovery of information without a query is becoming more and more important to people.

05 Mar 07

LibraryThing recommendations on Abebooks - LibraryThing

www.librarything.com/...rything-recommendations-on.php - Preview

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04 Feb 07

Me.dium Courts Controversy With Extreme Social Networking, Launched At DEMO

www.readwriteweb.com/..._extreme_social_networking.php - Preview

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Also, to facilitate the right interactions, it is important to reveal more
details about the other sites without much drilling. Perhaps a list of sites with
descriptions would be a good alternative user interface, as the moving network of nodes
is just too distracting.
Why not do the same online?
1 more annotations...
- Geographical proximity plays an important role, as we often strike
  conversations with complete strangers in order to find the "best deal"

17 Jan 07

与豆瓣杨勃聊天长尾推荐机制(by 黄锫坚) - 思维的乐趣BLOG

www.mindmeters.com/showlog.asp - Preview

douban google interview longtail pagerank recommendation yahoo 杨勃

其中的机制其实也不难理解，因为两本书有相同的读者群。
有效的推荐，是你挖掘出来的联系不是表面能看出来的。
3 more annotations...
- 我们也做过通过标签分析，这本书和哪本书最像，通过它做推荐，很准，但用处不大。
- 推荐光准确是没有用的，还要有效。
- 豆瓣，用的是人对书的投票
  >

15 Jan 07

The Reddit Recommendation Engine: Does It Work At All? - Dharmesh Shah's Personal Website

www.dharmesh.com/...ngine-Does-It-Work-At-All.aspx - Preview

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I know that reddit can’t divulge their “super-secret recommendation algorithm”, but I’m curious to know if its just me that is having the problem, or others are too.
Now, the common convention is to disclose the encryption algorithm as a way for the community to ensure that the approach is valid and legitimate.
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- Without having some idea of how the engine works, how do we trust it?
- How does the engine actually work? Recognizing that the algorithm is proprietary and private, I still have some high-level guesses.

17 Dec 06

EMI and Last.fm Launch User-Generated Music Map

www.coolfer.com/...emi_and_lastfm.php - Preview

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With 15 million unique users a month, 150,000 band biographies, and an amazing 65 million songs listed in its database
MSN Radio is now being powered by music recommendation engine Pandora. Microsoft may offer commissions for sharing music. Nokia has a music recommendation based on the picks of independent record stores around the world.
1 more annotations...
- The site makes recommendations artists, websites and retailers based on the comparison of the user's listening preferences and profiles of other users. Additonal content such as artist bios and site links will be given for EMI artists.

27 Nov 06

Personal recommendation software爌redicts consumer choice/The race to create a 'smart' Google - November 27, 2006

money.cnn.com/...8394347 - Preview

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The Web, they say, is leaving the era of search and entering one of discovery. What's the difference? Search is what you do when you're looking for something. Discovery is when something wonderful that you didn't know existed, or didn't know how to ask for, finds you.
But there is no go-to discovery engine - yet.
17 more annotations...
- Building a personalized discovery mechanism will mean tapping into all the manners of expression, categorization, and opinions that exist on the Web today.
- such a tool could change not just marketing, but all of commerce.
- The effect of recommender systems will be one of the most important changes in the next decade,
- tends to come off less like a trusted shopkeeper than a pushy salesman
- The new generation of recommenders will do better. Some employ filters that factor in more variables. Others analyze the contents of what they're recommending to grasp why you like something. A third category, hybrid recommenders, combines both strategies.
- Five weeks later, 37 registrants had already posted improvements to the Netflix system. Two contestants were just short of halfway to the goal.
- They'll draw on their knowledge of cinema and their experience categorizing hundreds of films - by star power, plot complexity, etc. - at What to Rent. "When you watch a movie, you interact with it like you're interacting with another person," Kuhlke says. "You're forming a relationship."
- Unlike collaborative filtering engines, Pandora understands each song in its database. Forty-five analysts, many with music degrees, rank 15,000 songs a month on 400 characteristics to gain a detailed grasp of each.
- An ongoing study conducted by Rentfrow at www.outofservice.com, where 90,000 people have taken a music/personality quiz, pushes the point even further, tying taste to political leanings, demographics, lifestyle, favorite authors - movies even.
- music recommenders can now draw lines from musical tastes to all sorts of things
- But the connection could have broad applications in business. Goodbye, context-based advertising. Hello, personality-based advertising.
- Steve Johnson, CEO of Boston-based recommender system ChoiceStream, which provides the collaborative filtering engine behind AOL.com, Blockbuster.com, iTunes and Directv.com
- Most consumers seem perfectly willing to trade preference data for insight. "What's fueling this is the desire for self-expression,
- photos are just a way to get Slide users communicating, establishing relationships,
- he's building a "machine that knows more about you than you know about yourself."
- He's using what he learned at PayPal, not to root out fraud but to create the best recommender system he can imagine, one that will cover the entire Web, pulling content of all kinds - music, movies, gadgets, blogs, news stories, cars, one-night stands, you name it - filtering it according to individual preference and delivering it to the desktop.
- Levchin cross-referenced traffic patterns, auction histories, user interactions, geography and a thousand other factors to root out fraud. "I would obsess on multivariate analysis. I'd have printouts and printouts graphing the relationship between any two variables