Tailrank is a service that finds the best content from millions of blogs and provides a custom ranking specific to the user. Tailrank also offers a mobile version and users can import blog (OPML) subscriptions to build a personalized reading list.
Tailrank, founded by Kevin Burton, filters news according to your interests and those of others. Tailrank allows each user to share news and blogs with other members of the site. Tailrank finds the internet's hottest channels by indexing over 24M weblogs and feeds per hour. Tailrank uses Spinn3r, which is a blog spider that can be specialized using your own behavior insteading of creating a separate crawler. The Spinn3r API is free to researchers. The hottest stories are discovered by tracking conversations between blogs. Tailrank takes into consideration linking behavior, the text of the post, links in common with other users, text relevance, weblog ranking, past performance, and various other factors for recommendations. The ranking algorithm finds weblogs that are highly linked and discussed links and citations. The biggest challenge facing Tailrank is the amount of data that needs to be processed and the need to keep that data consistent within a distributed system, about 52TB of raw blog content a month and continuous processing of 160Mbits of IO.
Though Tailrank states that it primarily uses collaborative filtering, as noted on it's website, I'm led to believe there is some content filtering involved as well because it searches the text of the post and uses that information to make recommendations.
What I've found even more interesting is a blog that Tailrank Goes Blank, But Nobody Notices! It stated in July that the technology section of the site had no news whatsoever, it was completely blank and at one point the rest of the site went blank too. Michael Arrington, the blogger, emailed Kevin Burton who responded they were in the middle of an infrastructure upgrade. Michael noted that shortly after the site went live again but the stories were days old. Interestingly Michael write, "the fact that the technology ssection was down for weeks and no one seemed to notice or write about it suggests that the site isn't being read regularly by very many people...Tailrank is basically a showcase for the technology behind his other startup, Spinn3r, which provides blog indexing and ranking services to other sites. If Tailrank can't stay up to date with the news, how can partners rely on the underlying technology?" Which is indeed a valid point!
Tailrank has 5 tabs: General, Technology, Politics, Entertainment and Video. When I registered on the site, I attempted to click on all the tabs and actually the Entertainment section was not available. After checking back 20 minutes later it was available. I also noticed that while Tailrank claims to spider only "blogs", an article I clicked on regarding the total lunar eclipse directed me to CNN.com/technology. So I imagine all websites are being scanned for news at this point. Just to test out this theory, I clicked on a few more links regarding the lunar eclipse and was directed to articles on msnbc, usatoday and thestar.com. I also noticed the timeframes on the suggested readings. The most recent article was 16 hours ago and the oldest was 24 hours ago, with only 8 recommendations on the page. While the articles were relevant, I was rather disappointed to see "old" items. Given the lunar eclipse, ,which I watched and found completely fascinating, happened last night, I expected to see articles that were written less than 12 hours ago at best since the eclipse ended a little after midnight. The other thing I just noticed is that I clicked on the entertainment tab and was offered information regarding the lunar eclipse though most of the sites that the articles were snipped from where found under the technology section...and there is a technology tab on Tailrank so I wonder why the articles weren't placed there instead. Another interesting point that I noticed is the somewhat biasness of the recommendations. For instance, I clicked on the Politics tab and received an overwhelming amount of information regarding John McCain. Apparently, the majority of users are avid McCain followers (which explains the recommendations if we're using a collaborative system) or Tailrank has incorporated some type of baseline information into the system that automatically thinks/suggests certain items such as McCain. I'm actually not a McCain follower so maybe thats why Im a little disenchanted with the recommendations but I can't imagine I'm the only Tailrank user that has a different opinion be it with Politics, Technology or Entertainment...ok, I think that's enough nitpicking for now! lol
Tailrank.com (user info: oneup/oneup)
Tailrank Architecture - Learn How to Track Memes Across the Entire Blogosphere (Nov 19, 2007)
Tailrank Goes Blank, but Nobody Notices (Jul 2, 2007)
Tailrank: A Social News Recommendation and Filtering System Gets a New Look (Jan 12, 2006)
Tailrank - Social News Recommendation Engine (Nov 10, 2005)
Tailrank - A Tool for the Long Tail (Sept 21, 2005)
Tailrank - About Us
Thursday, February 21, 2008
Thursday, February 14, 2008
Smart Phone Tells You What To Do
Articles Written Between Sept and Oct 2007
Dai Nippon Printing (DNP) and Palo Alto Research Center Inc (PARC) have been collaborating since 2005 to develop a context and activity aware system that recommends information about "local area" activities, such as shopping and dining, movies and bookstores or concerts and bars, matching the consumer's location, time of day and personal tastes. PARC named the software Magitti (derived from two early design concepts: a magic scope and a digital graffiti system). When this software is installed on your GPS-enabled phone Magitti starts to suggest what to do in your area. When an individual accesses their phone they will instantly see a list of recommendations. If it's noon, the osftware might suggest local restaurants. If it's 3PM, it might recommend a nearby boutique for shopping. If it's 9PM, a list of pubs might appear. Of particular interest to mobile recommendation, is its usefulness in unfamiliar areas to the user. For tourists, vacationers, business travel or even exploring unchartered areas of your own city, mobile recommendations prevent users from having to wander foreign streets and ask someone passing by for directions or their opinion, which has the potential to be a nightmare!
Interestingly, "Magitti pulls GPS data from a user's phone, as well as text messages, emails and information about events saved in the phone's calendar, and uploads it to a server, along with the user's search terms", says Kurt Partridge (a researcher at PARC). He states text messages are important bits of information because they often include data about future plans. If for instance a person gets a text message suggesting sushi, the software will put recommendations for sushi and Japanese restaurants higher on the list. The mere mention of text messages, emails and calendar notes being monitored and stored have raised several privacy concerns. PARC states these messages are only kept for a short amount of time but ultimately, there's a trade-off between privacy and convenience. Bo Begole, a co-leader on the project, remarked that the analysis happens on the handset and not on the servers at the company.
The software uses artificial-intelligence algorithms to make tailored recommendations. After reading several articles, I would infer this recommender system uses a hybrid model (context, demographic and collaborative). By making recommendations from text messages, emails and calendar notes and comparing them with location, time of day, and other personal tastes it seems to rely on context and demographic. I also read that collaborative filtering is used to recommend things that others with similar tastes like and allows people to input their own ratings and reviews.
Testers have noted the software works more often than not. One tester detailed at 11:30 am (pacific time) the system offered nearby lunch restaurants, a home furnishings store and a gym, noting it was rather easy to expand or limit the distance of suggestions and the type of cuisine. They likened the application to the Apple iPhone but commented the interface isn't nearly as slick. While the software is functional, there are still technical problems that have not been solved such as category ambiguity. Shopping could mean farmer's market or Macy's. Eating could mean sitting down at a restaurant or picking up a sandwich at the grocery store or enjoying a meal at home so the semantics of keywords still need to be analyzed for each individual in an effort to provide more accurate results.
Magitti will go through public trials with young adults in Tokyo, Spring 2008. Deployment is scheduled next year in Japan but it is unlikely this software will be sold in Europe or in the U.S.
DNP, PARC Jointly Develop Recommender System for Mobile Terminals
A Phone That Tells You What To Do
From PARC, The Mobile Phone As Tour Guide
Smart Phone Suggests Things To Do
Snapshots of Application
Dai Nippon Printing (DNP) and Palo Alto Research Center Inc (PARC) have been collaborating since 2005 to develop a context and activity aware system that recommends information about "local area" activities, such as shopping and dining, movies and bookstores or concerts and bars, matching the consumer's location, time of day and personal tastes. PARC named the software Magitti (derived from two early design concepts: a magic scope and a digital graffiti system). When this software is installed on your GPS-enabled phone Magitti starts to suggest what to do in your area. When an individual accesses their phone they will instantly see a list of recommendations. If it's noon, the osftware might suggest local restaurants. If it's 3PM, it might recommend a nearby boutique for shopping. If it's 9PM, a list of pubs might appear. Of particular interest to mobile recommendation, is its usefulness in unfamiliar areas to the user. For tourists, vacationers, business travel or even exploring unchartered areas of your own city, mobile recommendations prevent users from having to wander foreign streets and ask someone passing by for directions or their opinion, which has the potential to be a nightmare!
Interestingly, "Magitti pulls GPS data from a user's phone, as well as text messages, emails and information about events saved in the phone's calendar, and uploads it to a server, along with the user's search terms", says Kurt Partridge (a researcher at PARC). He states text messages are important bits of information because they often include data about future plans. If for instance a person gets a text message suggesting sushi, the software will put recommendations for sushi and Japanese restaurants higher on the list. The mere mention of text messages, emails and calendar notes being monitored and stored have raised several privacy concerns. PARC states these messages are only kept for a short amount of time but ultimately, there's a trade-off between privacy and convenience. Bo Begole, a co-leader on the project, remarked that the analysis happens on the handset and not on the servers at the company.
The software uses artificial-intelligence algorithms to make tailored recommendations. After reading several articles, I would infer this recommender system uses a hybrid model (context, demographic and collaborative). By making recommendations from text messages, emails and calendar notes and comparing them with location, time of day, and other personal tastes it seems to rely on context and demographic. I also read that collaborative filtering is used to recommend things that others with similar tastes like and allows people to input their own ratings and reviews.
Testers have noted the software works more often than not. One tester detailed at 11:30 am (pacific time) the system offered nearby lunch restaurants, a home furnishings store and a gym, noting it was rather easy to expand or limit the distance of suggestions and the type of cuisine. They likened the application to the Apple iPhone but commented the interface isn't nearly as slick. While the software is functional, there are still technical problems that have not been solved such as category ambiguity. Shopping could mean farmer's market or Macy's. Eating could mean sitting down at a restaurant or picking up a sandwich at the grocery store or enjoying a meal at home so the semantics of keywords still need to be analyzed for each individual in an effort to provide more accurate results.
Magitti will go through public trials with young adults in Tokyo, Spring 2008. Deployment is scheduled next year in Japan but it is unlikely this software will be sold in Europe or in the U.S.
DNP, PARC Jointly Develop Recommender System for Mobile Terminals
A Phone That Tells You What To Do
From PARC, The Mobile Phone As Tour Guide
Smart Phone Suggests Things To Do
Snapshots of Application
Thursday, February 7, 2008
Personal Recommendation Software Predicts Consumer Choice
Jeffrey O'Brien a writer for fortune magazine eats dinner with What to Rent! owners Matthew Kuhlke and Adam Geitgey. The site adminsters a personality test to visitors and recommends DVDS based on the findings. As a more difficult attempt during dinner, the two will each pick someone in the restaurant and without ever talking to them, divine their single favorite movie. What to Rent! categorizes hundreds of films by star power, plot complexity, etc. Offering that individuals watch movie in the same manner that they interact with other individuals, in essence, relationships between individuals and movies are formed. Geitgey scans the restaurant and selects a guy delivering meals and cleaning dishes wearing tattered jeans, a steel bracelet with a few tattoos. He appears to be in his late 20s, working in a youth-trendy restaurant in the part of the city where people that age who don't have real jobs hang out. Geitgey offers, "Those are the kind of guys who barely made it through high school because they couldn't focus, but spend most of their time reading light philosophy books by singers-turned-writers like Nick Cave." He adds, "he would be interested in things that have an underlying philosophy but are also physically intense" concluding Starship Troopers fits that exactly! Kuhlke spots a waitress in her late teens or early 20s, black hair in a bob, very cute but she looks at the floor as she walks and avoids eye contact with customers. "She's unhappy...working around all these jerks who just want to have sex with her" adds Kuhlke. He also suggests she wasn't popular in high school and is shaking off her past by working in a cool place. Kuhle then states "She's like an old-school romantic comedy but she'd pick Breakfast at Tifany's, a totally crappy movie. She's cool enough to know she should pick Audrey Hepburn, but not cool enough to pick the right movie, Roman Holiday" so he battles between the two movies and finally decides on Girl, Interrupted! lol
According to many researchers "We don't just buy products, we bond with them. We have relationships with our things. DVD collections, iTunes playlists, cars, cell phones. Each is an extension of who we are (or want to be). We put ourselves on display through our purchases, wearing our personalities on our sleeves, literally and figuratively, for the world to see." And the web is transitioning from a search tool to a discovery one. The difference is that searching 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! "Everything you buy online says a little bit about you. And if all those bits get put into one big trove of data about you and your tastes? Marketers heaven.
To dig a little deeper, I actually went to What To Rent! to test our their application. It seems to be a content based recommender because it relies on an individuals personality and the content/factors of the movie to offer recommendations opposed to a rating system as seen in typical collaborative recommenders. The site uses the LaBarrie Theory, a movie viewer emotionally interacts with a film in the same manner that they interact with other human beings, to make suggestions. The site first decipher's a users' personality then forms a general model of how thety react to the world and their average emotional state by providing an upfront personality test. The cluser then compares your ideal stimulus and current mood to the possible relationships with movies on file. The film that maximizes the desired criteria is recommended. One of the major problems with personality tests is that users often lie (intentionally or unintentionally) about who they are and will answer questions as if they behaved in their ideal manner..leading to inaccurate responses. The system, instead, asks about seeminly unimportant life experiences an in attempt to get more accurate responses. After answering the 20 question personality test and 2 addt'l questions regarding my current movie and type of movie I wanted to watch, I was presented with their first recommendatino....Goodfellas a 1990 movie!! Definitely not what I had in mind. It's a movie that I have seen before and was hardly in the mood to see again. The next movie presented is Usual Suspects, this is in fact one of my favorites and I'm probably guilty of over-watching it at this point because Im not in the mood to see at right now either. In order, I was then presented: Raiders of the Lost Ark (1981), Kindergarten Cop (1990), The Shawshank Redemption (1994), and Jackie Brown (1997). Growing tired of their recommendations, I finally convinced myself that Jackie Brown would do. Less because I genuinely wanted to watch the movie, and more about me trying to find a recommendation that will suffice! While their overall stats boast a user satisfaction of 80.27%, 1 of 6 (more like 0.25/6) in my humble opinion is hardly accurate enough to warrant return visits to this website. I think I'll opt for word of mouth recommendations instead!
And by the way, the guy with the tattoos is the restaurant manager and before exclaiming Brianna Loves Jenna (a porn movie) he exclaims Starship Troopers and the waitress, as it turns out, loves Roman Holiday...perhaps they would do a better job of selecting movies based on individual photographs than relying on the personality test!
The race to create a 'smart' Google
What To Rent! (username: prs-test)
According to many researchers "We don't just buy products, we bond with them. We have relationships with our things. DVD collections, iTunes playlists, cars, cell phones. Each is an extension of who we are (or want to be). We put ourselves on display through our purchases, wearing our personalities on our sleeves, literally and figuratively, for the world to see." And the web is transitioning from a search tool to a discovery one. The difference is that searching 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! "Everything you buy online says a little bit about you. And if all those bits get put into one big trove of data about you and your tastes? Marketers heaven.
To dig a little deeper, I actually went to What To Rent! to test our their application. It seems to be a content based recommender because it relies on an individuals personality and the content/factors of the movie to offer recommendations opposed to a rating system as seen in typical collaborative recommenders. The site uses the LaBarrie Theory, a movie viewer emotionally interacts with a film in the same manner that they interact with other human beings, to make suggestions. The site first decipher's a users' personality then forms a general model of how thety react to the world and their average emotional state by providing an upfront personality test. The cluser then compares your ideal stimulus and current mood to the possible relationships with movies on file. The film that maximizes the desired criteria is recommended. One of the major problems with personality tests is that users often lie (intentionally or unintentionally) about who they are and will answer questions as if they behaved in their ideal manner..leading to inaccurate responses. The system, instead, asks about seeminly unimportant life experiences an in attempt to get more accurate responses. After answering the 20 question personality test and 2 addt'l questions regarding my current movie and type of movie I wanted to watch, I was presented with their first recommendatino....Goodfellas a 1990 movie!! Definitely not what I had in mind. It's a movie that I have seen before and was hardly in the mood to see again. The next movie presented is Usual Suspects, this is in fact one of my favorites and I'm probably guilty of over-watching it at this point because Im not in the mood to see at right now either. In order, I was then presented: Raiders of the Lost Ark (1981), Kindergarten Cop (1990), The Shawshank Redemption (1994), and Jackie Brown (1997). Growing tired of their recommendations, I finally convinced myself that Jackie Brown would do. Less because I genuinely wanted to watch the movie, and more about me trying to find a recommendation that will suffice! While their overall stats boast a user satisfaction of 80.27%, 1 of 6 (more like 0.25/6) in my humble opinion is hardly accurate enough to warrant return visits to this website. I think I'll opt for word of mouth recommendations instead!
And by the way, the guy with the tattoos is the restaurant manager and before exclaiming Brianna Loves Jenna (a porn movie) he exclaims Starship Troopers and the waitress, as it turns out, loves Roman Holiday...perhaps they would do a better job of selecting movies based on individual photographs than relying on the personality test!
The race to create a 'smart' Google
What To Rent! (username: prs-test)
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