People have grown accustomed to being tracked online and shown ads for categories of products they have shown interest in, be it tennis or bank loans.

Increasingly, however, the ads tailored to them are for specific products that they have perused online. While the technique, which the ad industry calls personalized retargeting or remarketing, is not new, it is becoming more pervasive as companies like Google and Microsoft have entered the field. And retargeting has reached a level of precision that is leaving consumers with the palpable feeling that they are being watched as they roam the virtual aisles of online stores.

In remarketing, when a person visits an e-commerce site and looks at say, an Etienne Aigner Athena satchel on eBags.com, a cookie is placed into that person’s browser, linking it with the handbag. When that person, or someone using the same computer, visits another site, the advertising system creates an ad for that very purse.

Traditionally, online campaign effectiveness has been measured by "clicks" ... However, many display ads are not click-able ... and some campaigns hope to build longer-term interest in the brand rather than drive immediate response. Counting clicks alone then misses much of the value of the campaign.

Better measures of campaign effectiveness are based on the change in online brand awareness ... [due] to the display ad campaign alone. We ... [find] the change in probability that a user searches for brand terms or navigates to brand sites that can be attributed to an online ad campaign.

Randomized experiments ... are the gold standard for estimating treatment effects ... [but it] requires an advertiser to forego showing ads to some users ... [which] advertisers are not keen to [do] ... Estimation without randomization is more difficult but not always impossible .... Simply put, the controls [we pick] were eligible to be served campaign ads but were not.

Our estimates require summary (not personally identifiable) data on exposed and controls. The summary data are obtained from several sources, including the advertiser's own campaign information, ad serving logs, and sampled data from users who have installed Google toolbar and opted in to enhanced features.

A market for lemons is a market where the sellers cannot evaluate beforehand the quality of the goods that they are buying. So, if you have two types of products (say good workers and low quality workers) and cannot tell who is whom, the price that the buyer is willing to pay will be proportional to the average quality of the worker.

So the offered price will be between the price of a good worker and a low quality worker. What a good worker would do? Given that good workers will not get enough payment for their true quality, they leave the market. This leads the buyer to lower the price even more towards the price for low quality workers. At the end, we only have low quality workers in the market (or workers willing to work for similar wages) and the offered price reflects that.

This is exactly what is happening on Mechanical Turk today. Requesters pay everyone as if they are low quality workers, assuming that extra quality assurance techniques will be required on top of Mechanical Turk.

So, how can someone resolve such issues? The basic solution is the concept of signalling. Good workers need a method to signal to the buyer their higher quality. In this way, they can differentiate themselves from low quality workers.

Unfortunately, Amazon has not implemented a good reputation mechanism. The "number of HITs worked" and the "acceptance percentage" are simply not sufficient signalling mechanisms.

One thing Google doesn't do very well is provide the shopping-as-adventure experience ... You might go to the mall with a specific product in mind, but a well-designed mall ... forces you to discover -- and hopefully purchase -- other products that you might not have even known you wanted: the marketing types like to call this "serendipity." Google wants to be known as a destination for that kind of experience, said Sameer Samat, director of product management.

After years of trying and failing to reach that goal, Google plans to give it another go over the coming months. Don't expect Google to turn into a full-blown online retailer among the likes of Amazon.com or Buy.com just yet. But the combination of personalized features for product search pages and what Samat thinks is "the largest database of products that has been created" could entice people to actually shop on Google.

Google's current approach works best for those who are on a mission when they shop, shoppers who already know what they want and are just looking for additional information before sealing the deal ... [But] there are millions of other people who treat shopping as leisure, rather than a simple transaction. These are people who ... prefer browsing to targeted shopping, knowing that every now and then they'll discover something totally unique or completely unexpected.

Google wants to serve more of those people ... [by making] recommendations based on that list of products and lists submitted by others to help you discover new products: sort of like Amazon's recommendations page meets Pandora's radio stations meets Google.

"Shopping is not just about search, it's not just about intent, it's about discovery," Samat said. "If we can do it, and do it well, we will have built something that's really amazing; it should be the most comprehensive experience for shopping you could ever find."

Shared photos can be seen as billions of geo-temporal breadcrumbs that can promisingly serve as a latent source reflecting the trips of millions of users ... [We] automatically construct travel itineraries at large scale from those breadcrumbs.

By analyzing these breadcrumbs associated with a person's photo stream, one can deduce the cities visited by a person, which Points of Interest (POI) that the person took photos at, how long that person spent at each POI, and what the transit time was between POIs visited in succession.

By aggregating such timed paths of many users, one can construct itineraries that reflect the "wisdom" of touring crowds. Each such itinerary is composed of a sequence of POIs, with recommended visit times and approximate transit times between them.

[In surveys] users perceive our automatically generated itineraries to be as good as (or even slightly better than) itineraries provided by professional tour companies.

When you talk about the Internet growing to 225 million sites, you've got to ask: Who's parsing all that? How do you make sense of all that stuff?

I mean, who has time to wander all over the Internet?

Tomorrow's Yahoo! is going to be really tailored. I'm not talking about organization — organizing means that you already know what you want and somebody's just putting it in shape for you. I'm talking about both smart science and people culling through masses of information on the fly and figuring out what people want to know.

We will be delivering your interests to you. For instance, if you're a sports fan but have no interest in tennis, we won't show you tennis. We would know that you do things in a certain sequence, so we'd say, "Here's your portfolio. Here's some news you might like. Oh, you went to this movie last week, here's some other movies you might want to check out."

I call it the Internet of One. I want it to be mine, and I don't want to work too hard to get what I need. In a way, I want it to be HAL. I want it to learn about me, to be me, and cull through the massive amount of information that's out there to find exactly what I want.

"If you were starting from scratch, you could never possibly justify [the current] business model," [Google Chief Economist] Hal Varian said ... "Grow trees -- then grind them up, and truck big rolls of paper down from Canada? Then run them through enormously expensive machinery, hand-deliver them overnight to thousands of doorsteps, and leave more on newsstands, where the surplus is out of date immediately and must be thrown away? Who would say that made sense?" The old-tech wastefulness of the process is obvious, but Varian added a less familiar point. Burdened as they are with these "legacy" print costs, newspapers typically spend about 15 percent of their revenue on what, to the Internet world, are their only valuable assets: the people who report, analyze, and edit the news.

"Nothing that I see suggests the 'death of newspapers,'" [Google CEO] Eric Schmidt told me. The problem was the high cost and plummeting popularity of their print versions. "Today you have a subscription to a print newspaper," he said. "In the future model, you'll have subscriptions to information sources that will have advertisements embedded in them, like a newspaper. You'll just leave out the print part. I am quite sure that this will happen ... As print circulation falls, the growth of the online audience is dramatic ... Newspapers don't have a demand problem; they have a business-model problem." Many of his company’s efforts are attempts to solve this, so that newspaper companies can survive, as printed circulation withers away.

The three pillars of the new online business model, as I heard them invariably described, are distribution, engagement, and monetization. That is: getting news to more people, and more people to news-oriented sites; making the presentation of news more interesting, varied, and involving; and converting these larger and more strongly committed audiences into revenue, through both subscription fees and ads.

The best monetizing schemes are of course ones that people like -- ads they enjoy seeing, products for which they willingly pay. Online display ads should be better on these counts too, [Google VP Neal] Mohan said. "here are things we can do online that we simply can't do in print," he said. An ad is "intrusive" mainly if it is not related to what you care about at that time ... "The online world will be a lot more attuned to who you are and what you care about" ... Advertising has been around forever, Mohan said, "but until now it has always been a one-way conversation."

Pages similar to: www.engadget.com
Gizmodo ... gizmodo.com
Ubergizmo ... ubergizmo.com
Wired ... wired.com
Lifehacker ... lifehacker.com

[The] previous Google News recommendation system was developed using a collaborative filtering method. It recommends news stories that were read by users with similar click history. This method has two major drawbacks ... First, the system cannot recommend stories that have not yet been read by other users ... Second ... news stories [that] are generally very popular ... are constantly recommended to most of the users, even for those users who never [are interested because] ... there are always enough clicks ... to make the recommendation.

A solution to these two problems would be to build profiles of user's genuine interests and use them to make news recommendations. The profiles ... filter out the stories that are not of interest ... [and recommend stories] even if [they have] not been clicked on by other users ... Based on a user's news reading history, the recommender predicts the topic categories of interest ... News articles in those categories are ranked higher in the candidate list.

On average, the hybrid method ... improves the CTR [of] the existing collaborative method by 30.9% ... [and increased] the frequency of website visits in the test group [by] 14.1%.

With Aardvark, users ask a question, either by instant message, email, web input, text message, or voice. Aardvark then routes the question to the person in the user's extended social network most likely to be able to answer that question.

Aardvark queries tend to be long, highly contextualized, and subjective -- in short, they tend to be the types of queries that are not well-serviced by traditional search engines. We also find that the vast majority of questions get answered promptly and satisfactorily, and that users are surprisingly active, both in asking and answering.

50.6% ... used their status messages to ask a question .... [on] sites like Facebook and Twitter .... 24.3% received a response in 30 minutes or less, 42.8% in one hour or less, and 90.1% within one day .... 69.3% ... who received responses reported they found the responses helpful.

The most common reason to search socially, rather than through a search engine, was that participants had more trust in the responses provided by their friends [24.8%]. A belief that social networks were better than search engines for subjective questions, such as seeking opinions or recommendations, was also a common explanation [21.5%].

The most common motivation given for responding to a question was altruism [37.0%]. Expertise was the next biggest factor [31.9%], with respondents being motivated because they felt they had special knowledge of the topic ... Nature of the relationship with the asker was an important motivator [13.7%], with closer friends more likely to get answers ... [as well as] the desire to connect socially [13.5%] ... free time [12.3%] ... [and] earning social capital [10.5%].

Many indicated they would prefer a face-to-face or personal request, and ignored questions directed broadly to the network-at-large .... [But] participants enjoyed the fun and social aspects of posing questions to their networks.

It is often suggested that users are hopelessly lazy and unmotivated on security ... [This] is entirely rational from an economic perspective ... Most security advice simply offers a poor cost-benefit tradeoff to users and is rejected.

[Security] advice offers to shield [people] from the direct costs of attacks, but burdens them with increased indirect costs ... Since victimization is rare, and imposes a one-time cost, while security advice applies to everyone and is an ongoing cost, the burden ends up being larger than that caused by the ill it addresses.

To make this concrete, consider an exploit that affects 1% of users annually, and they waste 10 hours clearing up when they become victims. Any security advice should place a daily burden of no more than 10/(365 * 100) hours or 0.98 seconds per user in order to reduce rather than increase the amount of user time consumed. This generated the profound irony that much security advice ... does more harm than good.

We estimate US annual phishing losses at $60 million ... Even for minimum wage any advice that consumes more than ... 2.6 minutes per year to follow is unprofitable [for users] from a cost benefit point of view ... Banks [also] have more to fear from ... indirect losses such as support costs ... than direct losses. For example ... an agent-assisted password reset by 10% of their users would cost $48 million, easily dwarfing Wells Fargo's share of the overall $60 million in phishing losses.

Users are effectively trained to ignore certificate warnings by seeing them repeatedly when there is no real security threat .... As far as we can determine, there is no evidence of a single user being saved from harm by a certificate error, anywhere, ever ... The idea that certificate errors are a useful tool in protecting [users] from harm ... is entirely abstract and not evidence-based. The effort we ask of [users] is real, while the harm we warn them of is theoretical.

Advice almost always ignores the cost of user effort ... The main reason security advice is ignored is that it makes an enormous miscalculation: it treats as free a resource that is actually worth $2.6 billion an hour ... Advice-givers and policy-mandaters demand far more effort than any user can give .... User education is a cost borne by the whole population, while offering benefit only to the fraction that fall victim ... The cost of any security advice should be in proportion to the victimization rate .... [lest] in trying to defend against everything we end up defending nothing.

It is not users who need to be better educated on the risks of various attacks, but the security community. Security advice simply offers a bad cost-benefit tradeoff to users .... We must respect users' time and effort. Viewing the user's time as worth $2.6 billion an hour is a better starting point than valuing it at zero ... When we exaggerate all dangers we simply train users to ignore us.

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The most obvious way that a search tool can improve the user experience given the prevalence of re-finding is for the tool to explicitly remember and expose that user's search history.

Certain aspects of a person's history may be more useful to expose ... For example, results that are re-found often may be worth highlighting ... The result that is clicked first ... is more likely to be useful later, and thus should be emphasized, while results that are clicked in the middle may be worth forgetting ... to reduce clutter. Results found at the end of a query session are more likely to be re-found.

The query used to re-find a URL is often better than the query used initially to find it ... [because of] how the person has come to understand this result. [Emphasize] re-finding queries ... in the history ... The previous query may even be worth forgetting to reduce clutter.

When exposing previously found results, it is sometimes useful to label or name those results, particularly when those results are exposed as a set. Re-finding queries may make useful labels. A Web browser could even take these bookmark queries and make them into real bookmarks.

A previously found result ... may be what the person is looking for ... even when the result [normally] is not going to be returned ... For example, [if] the user's current query is a substring of a previous query, the search engine may want to suggest the results from the history that were clicked from the longer query. In contrast, queries that overlap with but are longer than previous queries may be intended to find new results.

[An] identical search [is] highly predictive of a repeat click ... [We] can treat the result specially and, for example, [take] additional screen real estate to try to meet the user's information need with that result ... [with] deep functionality like common paths and uses in [an expanded] snippet. For results that are re-found across sessions, it may make sense instead to provide the user with deep links to [some] new avenues within the result to explore.

At the beginning of a session, when people are more likely to be picking up a previous task, a search engine should provide access into history. In the middle of the session ... focus on providing access to new information or new ways to explore previously viewed results. At the end of a session ... suggest storing any valuable information that has been found for future use.

The decision to build the original GFS around [a] single master really helped get something out the door ... more rapidly .... [But] problems started to occur ... going from a few hundred terabytes up to petabytes, and then up to tens of petabytes ... [because of] the amount of metadata the master had to maintain.

[Also] when you have thousands of clients all talking to the same master at the same time ... the average client isn't able to command all that many operations per second. There are applications such as MapReduce, where you might suddenly have a thousand tasks, each wanting to open a number of files. Obviously, it would take a long time to handle all those requests and the master would be under a fair amount of duress.

64MB [was] the standard chunk size ... As the application mix changed over time, however, ways had to be found to let the system deal efficiently with large numbers of files [of] far less than 64MB (think in terms of Gmail, for example). The problem was not so much with the number of files itself, but rather with the memory demands all those [small] files made on the centralized master .... There are only a finite number of files you can accommodate before the master runs out of memory.

Many times, the most natural design for some application just wouldn't fit into GFS -- even though at first glance you would think the file count would be perfectly acceptable, it would turn out to be a problem .... BigTable ... [is] one potential remedy ... [but] I'd say that the people who have been using BigTable purely to deal with the file-count problem probably haven't been terribly happy.

The GFS design model from the get-go was all about achieving throughput, not about the latency at which it might be achieved .... Generally speaking, a hiccup on the order of a minute over the course of an hour-long batch job doesn't really show up. If you are working on Gmail, however, and you're trying to write a mutation that represents some user action, then getting stuck for a minute is really going to mess you up. We had similar issues with our master failover. Initially, GFS had no provision for automatic master failure. It was a manual process ... Our initial [automated] master-failover implementation required on the order of minutes .... Trying to build an interactive database on top of a file system designed from the start to support more batch-oriented operations has certainly proved to be a pain point.

They basically try to hide that latency since they know the system underneath isn't really all that great. The guys who built Gmail went to a multihomed model, so if one instance of your GMail account got stuck, you would basically just get moved to another data center ... That capability was needed ... [both] to ensure availability ... [and] to hide the GFS [latency] problems.

The model in GFS is that the client just continues to push the write until it succeeds. If the client ends up crashing in the middle of an operation, things are left in a bit of an indeterminate state ... RecordAppend does not offer any replay protection either. You could end up getting the data multiple times in a file. There were even situations where you could get the data in a different order ... [and then] discover the records in different orders depending on which chunks you happened to be reading ... At the time, it must have seemed like a good idea, but in retrospect I think the consensus is that it proved to be more painful than it was worth. It just doesn't meet the expectations people have of a file system, so they end up getting surprised. Then they had to figure out work-arounds.

Acknowledging that Google hasn't always been open about how it mines the roughly 800 terabytes of personal data it has gathered since 1998, [CEO Eric] Schmidt apologized to users -- particularly the 1,237,948 who take daily medication to combat anxiety --for causing any unnecessary distress, and he expressed regret -- especially to Patricia Fort, a single mother taking care of Jordan, Sam, and Rebecca, ages 3, 7, and 9 -- for not doing more to ensure that private information remains private.

Monday's apology comes after the controversial launch of Google Buzz, a social networking platform that publicly linked Gmail users to their most e-mailed contacts by default.

"I'd like nothing more than to apologize in person to everyone we've let down, but as you can see, many of our users are rarely home at this hour," said Google cofounder and president Sergey Brin, pointing to several Google Map street-view shots of empty bedroom and living room windows on a projection screen behind him. "And, if last night's searches are any indication, Boston's Robert Hornick is probably out shopping right now for the spaghetti and clam sauce he'll be cooking tonight ... Either that, or hunting down that blond coworker of his, Samantha, whose Picasa photos he stares at every night."

Instead of a "one size fits all" model, the web offers a "market of one" ... [powered by] suggestions of things to buy based on your previous purchases, or on purchases of customers like you.

Not only content, but prices may also be personalized, leading to various forms of differential pricing ... [But] the ability of firms to extract surplus [may be] quite limited when consumers are sophisticated ... [And] perfect price description and free entry ... pushes profits to zero, conferring all benefits to the customers.

The same sort of personalization can occur in advertising ... Google and Yahoo ... [already] allow users to specify their areas of interest and then see ads related to those interests. It is also relatively common for advertisers ... to show ads based on previous responses of users to related ads.

[Google Fellow Amit] Singhal notes that the engineers in Building 43 are exploiting ... the hundreds of millions who search on Google. The data people generate when they search -- what results they click on, what words they replace in the query when they're unsatisfied, how their queries match with their physical locations -- turns out to be an invaluable resource in discovering new signals and improving the relevance of results.

"On most Google queries, you're actually in multiple control or experimental groups simultaneously," says search quality engineer Patrick Riley. Then he corrects himself. "Essentially," he says, "all the queries are involved in some test." In other words, just about every time you search on Google, you're a lab rat.

This flexibility -- the ability to add signals, tweak the underlying code, and instantly test the results -- is why Googlers say they can withstand any competition from Bing or Twitter or Facebook. Indeed, in the last six months, Google has [found and] made more than 200 improvements.

Many of you wanted to see even more personalized recommendations ... [Now], we've started inserting items selected just for you inside the Recommended items section. This is great if you've got interests that are less mainstream. If you love Lego robots, for example, then you should start to notice more of them in your Recommended items.