Quick links

Some of what has caught my attention recently:

• Oldest example I could find of the "PC is dead" in the press, a New York Times article from 1992. If people keep making this prediction for a few more decades, eventually it might be right. ([1])
  
  
• Amazon CEO Jeff Bezos says to innovate, you have to try many things, fail but keep trying, and be "willing to be misunderstood for long periods of time". ([1])
  
  
• Median tenure at Amazon and Facebook is a year or less (in part due to their massive recent hiring). Also, most people at Facebook have never worked anywhere other than Facebook. ([1])
  
  
• Spooky research out of UW CS and Google that crowdsources surveillance, finding all the Flickr photos from an big event like a concert that happen to include a specific person (no matter at what angle or from what location the crowd of people at the event took the pictures). ([1])
  
  
• You can scan someone's fingerprints from 6 feet away and copy their keys from 200 feet away. ([1] [2])
  
  
• Pretty impressive valuations incubator Y Combinator is getting on its startups: "The combined value of the top 21 companies is $4.7 billion."([1])
  
  
• But even even for some of the more attractive small startups to acquire, those out of Y Combinator, odds of acquisition still are only about 8%, and most of those will be relatively low valuation talent acquisitions. Sometimes it can seem like everyone is getting bought, but it is only a fortunate few who have the right combination of product, team, timing, luck, and network.([1])
  
  
• Someone going solidly for the dumbphone market, which is by far the biggest market still, with a snazzy but simple and mostly dumb phone. That's smart. ([1] [2])
  
  
• Google Scribe makes suggestions for what you are going to type next when you are writing documents. Try starting with "All work and" ([1]).
  
  
• When I started my blog and called it "Geeking with Greg", the word "geek" still had pretty negative connotations, especially in the mainstream. A decade later, things have changed. ([1])
  
  
• Not surprising people don't use privacy tools since the payoff is abstract and the tools require work for the average user to understand and use. What surprises me more is that more people don't use advertising blocking tools like AdBlock. ([1])
  
  
• The sad story of why Google never launched GDrive. ([1])
  
  
• Carriers are going to be upset about Apple's plans to disrupt text messaging. Those overpriced plans are a big business for carriers. ([1])
  
  
• It would be great if Skype acquisition was part of a plan to disrupt the mobile industry by launching a mobile phone that always picks the lowest cost data network (including free WiFi networks) available. Consumers would love that; it could lower their monthly bills by an order of magnitude. ([1] [2])
  
  
• Social data is of limited use in web search because there isn't much data from your friends. Moreover, the best information about what is a good website for you almost certainly comes from people like you who you might not even know, not from the divergent tastes of your small group of friends. As Chris Anderson (author of The Long Tail) said, "No matter who you are, someone you don't know has found the coolest stuff." ([1] [2])
  
  
• Customization (aka active personalization) is too much work. Most people won't do it. If you optimize for the early adopter tinkerer geeks who love twiddling knobs, you're designing a product that the mainstream will never use. ([1])
  
  
• If you launch a feature that just makes your product more complicated and confusing to most customers, you would have been better off doing nothing at all. Success is not launching things, but launching things that help customers. ([1])
  
  
• Google News shifts away from clustering and toward personalization. ([1] [2])
  
  
• Crowdsourcing often works better when unpaid ([1])
  
  
• Eli Pariser is still wrong. ([1])

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Continuous profiling at Google

"Google-Wide Profiling: A Continuous Profiling Infrastructure for Data Centers" (PDF) has some fascinating details on how Google does profiling and looks for performance problems.

From the paper:

GWP collects daily profiles from several thousand applications running on thousands of servers .... At any moment, profiling occurs only on a small subset of all machines in the fleet, and event-based sampling is used at the machine level .... The system has been actively profiling nearly all machines at Google for several years.

Application owners won't tolerate latency degradations of more than a few percent .... We measure the event-based profiling overhead ... to ensure the overhead is always less than a few percent. The aggregated profiling overhead is negligible -- less than 0.01 percent.

GWP profiles revealed that the zlib library accounted for nearly 5 percent of all CPU cycles consumed ... [which] motivated an effort to ... evaluate compression alternatives ... Given the Google fleet's scale, a single percent improvement on a core routine could potentially save significant money per year. Unsurprisingly, the new informal metric, "dollar amount per performance change," has become popular among Google engineers.

GWP profiles provide performance insights for cloud applications. Users can see how cloud applications are actually consuming machine resources and how the picture evolves over time ... Infrastructure teams can see the big picture of how their software stacks are being used ... Always-on profiling ... collects a representative sample of ... [performance] over time. Application developers often are surprised ... when browsing GWP results ... [and find problems] they couldn't have easily located without the aggregated GWP results.

Although application developers already mapped major applications to their best [hardware] through manual assignment, we've measured 10 to 15 percent potential improvements in most cases. Similarly ... GWP data ... [can] identify how to colocate multiple applications on a single machine [optimally].

One thing I love about this work is how measurement provided visibility and motivated people. Just by making it easy for everyone to see how much money could be saved by making code changes, engineers started aggressively going after high value optimizations and measuring themselves on "dollar amount per performance change".

For more color on some of the impressive performance work done at Google, please see my earlier post, "Jeff Dean keynote at WSDM 2009".

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Eli Pariser is wrong

In recent interviews and in his new book, "The Filter Bubble", Eli Pariser claims that personalization limits serendipity and discovery.

For example, in one interview, Eli says, "Basically, instead of doing what great media does, which is push us out of our comfort zone at times and show us things that we wouldn't expect to like, wouldn't expect to want to see, [personalization is] showing us sort of this very narrowly constructed zone of what is most relevant to you." In another, he claims, personalization creates a "distorted view of the world. Hearing your own views and ideas reflected back is comfortable, but it can lead to really bad decisions--you need to see the whole picture to make good decisions."

Eli has a fundamental misunderstanding of what personalization is, leading him to the wrong conclusion. The goal of personalization and recommendations is discovery. Recommendations help people find things they would have difficulty finding on their own.

If you know about something already, you use search to find it. If you don't know something exists, you can't search for it. And that is where recommendations and personalization come in. Recommendations and personalization enhance serendipity by surfacing useful things you might not know about.

That is the goal of Amazon's product recommendations, to help you discover things you did not know about in Amazon's store. It is like a knowledgeable clerk who walks you through the store, highlighting things you didn't know about, helping you find new things you might enjoy. Recommendations enhance discovery and provide serendipity.

It was also the goal of Findory's news recommendations. Findory explicitly sought out news you would not know about, news from a variety of viewpoints. In fact, one of the most common customer service complaints at Findory was that there was too much diversity of views, that people wanted to eliminate viewpoints that they disagreed with, viewpoints that pushed them out of their comfort zone.

Eli's confusion about personalization comes from a misunderstanding of its purpose. He talks about personalization as narrowing and filtering. But that is not what personalization does. Personalization seeks to enhance discovery, to help you find novel and interesting things. It does not seek to just show you the same things you could have found on your own.

Eli's proposed solution is more control. But, as Eli himself says, control is part of the problem: "People have always sought [out] news that fits their own views." Personalization and recommendations work to expand this bubble that people try to put themselves it, to help them see news they would not look at on their own.

Recommendations and personalization exist to enhance discovery. They improve serendipity. If you just want people to find things they already know about, use search or let them filter things themselves. If you want people to discover new things, use recommendations and personalization.

Update: Eli Pariser says he will respond to my critique. I will link to it when he does.

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Taking small steps toward personalized search

Some very useful lessons in this work in a recent WSDM 2011 conference, "Personalizing Web Search using Long Term Browsing History" (PDF).

First, they focused on a simple and low risk approach to personalization, reordering results below the first few. There are a lot of what are essentially ties in the ranking of results after the first 1-2 results; the ranker cannot tell the difference between the results and is ordering them arbitrarily. Targeting the results the ranker cannot differentiate is not only low risk, but more likely to yield easy improvements.

Second, they did a large scale online evaluation of their personalization approach using click data as judgement of quality. That's pretty rare but important, especially for personalized search where some random offline human judge is unlikely to know the original searcher's intent.

Third, their goal was not to be perfect, but just help more often than hurt. And, in fact, that is what they did, with the best performing algorithm "improving 2.7 times more queries than it harms".

I think those are good lessons for others working on personalized search or even personalization in general. You can take baby steps toward personalization. You can start with minor reordering of pages. You can make low risk changes lower down to the page or only when the results are otherwise tied for quality. As you get more aggressive, with each step, you can verify that each step does more good than harm.

One thing I don't like about the paper is that they only investigated using long-term history. There is a lot of evidence (e.g. [1] [2]) that very recent history, your last couple searches and clicks, can be important, since they may show frustration in an attempt to satisfy some task. But otherwise great lessons in this work out of Microsoft Research.

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Quick links

Some of what has caught my attention recently:

• Apple captured "a remarkable 50% value share of estimated Q1/11 handset industry operating profits among the top 8 OEMs with only 4.9% global handset unit market share." ([1]). The iPhone generates 50% of Apple's revenue and even more of their profits. To a large extent, the company is the iPhone company. ([2] [3]) But, Gartner predicts iPhone market share will peak in 2011. ([4])
  
  
• Researchers find bugs in payment systems, order free stuff from Buy.com and JR.com. Disturbing that, when they contacted Buy.com to report the problem, Buy.com's accounting systems had the invoice as fully paid even though they never received the cash. ([1])
  
  
• Eric Schmidt says, "The story of innovation has not changed. It has always been a small team of people who have a new idea, typically not understood by people around them and their executives." ([1])
  
  
• Netflix randomly kills machines in its cluster all the time, just to make sure Netflix won't go down when something real kills their machines. Best part, they call this "The Chaos Monkey". ([1] [2])
  
  
• Hello, Amazon, could I borrow 1,250 of your computers for 8 hours? ([1])
  
  
• Felix Salmon says, "Eventually ... ad-serving algorithms will stop being dumb things based on keyword searches, and will start being able to construct a much more well-rounded idea of who we are and what kind of advertising we're likely to be interested in. At that point ... they probably won't feel nearly as creepy or intrusive as they do now. But for the time being, a lot of people are going to continue to get freaked out by these ads, and are going to think that the answer is greater 'online privacy'. When I'm not really convinced that's the problem at all." ([1])
  
  
• Not sure which part of this story I'm more amazed by, that Google offered $10B for Twitter or that Twitter rejected $10B as not enough. ([1])
  
  
• Apple may be crowdsourcing maps using GPS trail data. GPS trails can also be used for local recommendations, route planning, personalized recommendations, and highly targeted deals, coupons, and ads. ([1] [2] [3])
  
  
• Management reorg at Google. Looks like it knocks back the influence of the PMs to me, but your interpretation may differ. ([1] [2])
  
  
• If you use Google Chrome and go to google.com, you're using SPDY to talk to Google's web servers, not HTTP. Aggressive of Google and very cool. ([1] [2])
  
  
• Shopping search engines (like product and travel) should look for good deals in their databases and then help people find good deals ([1])
  
  
• When Apple's MobileMe execs started talking about what the poorly reviewed MobileMe was really supposed to do, Steve Jobs demanded, "So why the f*** doesn't it do that?", then dismissed the executives in charge and appointed new MobileMe leaders. ([1] [2])
  

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The value of Google Maps directions logs

Ooo, this one is important. A clever and very fun paper, "Hyper-Local, Direction-Based Ranking of Places" (PDF), will be presented at VLDB 2011 later this year by a few Googlers.

The core idea is that, when people ask for directions from A to B, it shows that people are interested in B, especially if they happen to be at or near A.

Now, certain very large search engines have massive logs of people asking for directions from A to B, hundreds of millions of people and billions of A to B queries. And, it appears this data may be as or more useful than user reviews of businesses and maybe GPS trails for local search ranking, recommending nearby places, and perhaps local and personalized deals and advertising.

From the paper:

A query that asks for directions from a location A to location B is taken to suggest that a user is interested in traveling to B and thus is a vote that location B is interesting. Such user-generated direction queries are particularly interesting because they are numerous and contain precise locations.

Direction queries [can] be exploited for ranking of places ... At least 20% of web queries have local intent ... [and mobile] may be twice as high.

[Our] study shows that driving direction logs can serve as a strong signal, on par with reviews, for place ranking ... These findings are important because driving direction logs are orders of magnitude more frequent than user reviews, which are expensive to obtain. Further, the logs provide near real-time evidence of changing sentiment ... and are available for broader types of locations.

What is really cool is that, not only is this data easier and cheaper to obtain than customer reviews, but also there is so much more of it that the ranking is more timely (if, for example, ownership changes or a place closes) and coverage much more complete.

I find it a little surprising that Google hasn't already heavily been using this data. In fact, the paper suggests that Google is only beginning to start using it. At the end of the paper, the authors write that they hope to investigate what types of queries benefit the most from this data and then look at personalizing the ranking based on each person's specific search and location history.

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Resurgence of interest in personalized information

There has been a lot of news about personalization and recommendations of information in the last week.

Google News launched additional implicit personalization of news based on your clickstream, bringing it yet another step closer to the Findory of seven years ago.

Yahoo reversed an earlier policy to keep data only for 90 days, now upping it to three years, because tossing data away was hurting their efforts to improve relevance and personalize their search, news, and advertising.

Hank Nothhaft writes at Mashable that the Facebook News Feed really needs to be personalized implicitly based on what you consume and surface news from across the web. He says it should not only deliver "the best and most relevant news and blog posts on my favorite topics and interests, but it also recommends deals and product information, things to do and even media like videos and podcasts, all picked just for me" (which, if implemented as described, might also make Facebook News Feed similar to Findory).

Finally, Mark Milian at CNN points to all the efforts at newspapers and startups on personalized news. What strikes me about these is how few focus on advertising, which is the core business of newspapers, and improving the usefulness and revenue of ads on news sites. Former Google CEO Eric Schmidt had some useful thoughts on that some time ago that are still worth reading for those working on personalizing information today.

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