Google Reader recommends articles

In a post on the official Google Reader blog, "May we recommend...", Laurence Gonsalves describes a new recommendation feature for Google Reader that recommends articles based on what you have read in the past. An excerpt:

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.

Sadly, no additional details appear to be available. In my usage, there were rare gems in the recommendations, but a lot of randomness, and a strong bias toward very popular items. The lack of explanation -- why was this item recommended? -- and lack of a way to correct the recommendations likely will make people less forgiving of these problems. I also saw recommendations for items I had already read; items you have already seen always should be filtered from recommendations.

For more on that, you might enjoy some of my previous posts on this topic, such as the Mar 2009 "What is a good recommendation algorithm?" and the much older Dec 2006 "The RSS beast".

Update: A couple weeks later, Google launches Google Reader Play, a StumbleUpon knock-off that recommends web content based on what you say you like. Googler Garrett Wu writes that "it uses the same technology as the Recommended Items feed in Reader." In my usage, it had the same problems too.

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New details on LinkedIn architecture

Googler Daniel Tunkelang recently wrote a post, "LinkedIn Search: A Look Beneath the Hood", that has slides from a talk by LinkedIn engineers along with some commentary on LinkedIn's search architecture.

What makes LinkedIn search so interesting is that the search does real-time updates (the "time between when user updates a profile and being able to find him/herself by that update need to be near-instantaneous"), faceted search (">100 OR clauses", "NOT support", complex boolean logic, some facets are hierarchical, some are dynamic over time), and personalized relevance ranking of search results (ordered by distance in your LinkedIn social graph).

LinkedIn appears to use a combination of aggressive partitioning, keeping data in-memory, and a lot of custom code (mostly modifications to Lucene, some of which have been released open source) to handle these challenges. One interesting tidbit is, going against current conventional wisdom, LinkedIn appears to only use caching minimally, preferring to spend their efforts and machine resources on making sure they can recompute computations quickly than on hiding poor performance behind caching layers.

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Gmail launches personalized ads

Google's popular mail service, GMail, has launched advertising targeted not just to the particular e-mail message you are reading, but to other e-mails you might have read recently. An excerpt:

Sometimes, there aren't any good ads to match to a particular message. From now on, you'll sometimes see ads matched to another recent email instead.

For example, let's say you're looking at a message from a friend wishing you a happy birthday. If there aren't any good ads for birthdays, you might see the Chicago flight ads related to your last email instead.

It is a significant move toward personalized advertising and, as the Google post notes, is a big change for Google, as they previously "had specified that ads alongside an email were related only to the text of the current message." For example, here, Google says, "Ads and links to related pages only appear alongside the message that they are targeted to, and are only shown when the Google Mail user ... is viewing that particular message."

For more on personalized ads that target not only the current content, but also to previously viewed content that has strong purchase intent, please see my July 2007 post, "What to advertise when there is no commercial intent?"

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Yahoo on personalizing content and ads

Yahoo CEO Carol Bartz had a few tidbits on personalized relevance for content and advertising in the recent Yahoo Q4 2009 earnings call. Some excerpts:

We generate value ... [through] the vast amount of data we gather and use to deliver a better, more personal experience for users and a better, more targeted audience for our advertisers.

Since we began paring our content optimization technology with editorial expertise we have seen click through rates in the Today module more than double ... We are making additional improvements to the technology that will make the user experience even more personally relevant.

Truth be told, no one has uncovered the holy grail of making advertising as relevant as content is 100% of the time. Beyond just offering advertisers a specific bucket, say women aged 35-45 and have children, we instead need to deliver many more specific attributes of scale. For example, women aged 35-45 with kids under three who are shopping for a minivan, and on and on and on and on. If we can do this we can create a better experience for both the user and the advertiser.

We have been letting great data about the consumers, data that is very attractive to advertisers fall to the floor ... We simply aren't even close to maximizing the value of our massive audience for advertisers.

Sounds like the goal is right, but the pace is slow. For more on that, please see also my June 2009 post, "Yahoo CEO Carol Bartz on personalization".

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Hybrid, not artificial, intelligence

Google VP Alfred Spector gave a talk last week at University of Washington Computer Science on "Research at Google". Archived video is available.

What was unusual about Al's talk was his focus on cooperation between computers and humans to allow both to solve harder problems than they might be able to otherwise.

Starting at 8:30 in the talk, Al describes this as a "virtuous cycle" of improvement using people's interactions with an application, allowing optimizations and features like like learning to rank, personalization, and recommendations that might not be possible otherwise.

Later, around 33:20, he elaborates, saying we need "hybrid, not artificial, intelligence." Al explains, "It sure seems a lot easier ... when computers aren't trying to replace people but to help us in what we do. Seems like an easier problem .... [to] extend the capabilities of people."

Al goes on to say the most progress on very challenging problems (e.g. image recognition, voice-to-text, personalized education) will come from combining several independent, massive data sets with a feedback loop from people interacting with the system. It is an "increasingly fluid partnership between people and computation" that will help both solve problems neither could solve on their own.

This being a Google Research talk, there was much else covered, including the usual list of research papers out of Google, solicitation of students and faculty, pumping of Google as the best place to access big data and do research on big data, and a list of research challenges. The most interesting of the research challenges were robust, high performance, transparent data migration in response to load in massive clusters, ultra-low power computing (e.g. powered only by ambient light), personalized education where computers learn and model the needs of their students, and getting outside researchers access to the big data they need to help build hybrid, not artificial, intelligence.

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Predictions for 2010

It's that time of year again. Many are making their predictions for the tech industry for 2010.

It's been a while since I played this game -- last time was my dark prediction for a dot-com crash in 2008 ([1] [2]) -- but I thought I'd try again this year.

I wrote up my predictions in a post over at blog@CACM, "What Will 2010 Bring?"

Because it is for the CACM, the predictions focus more on computing in general than on startups, recommendations, or search. And, they are phrased as questions than as predictions.

I think the answer to some of the questions I posed may be no. For example, I doubt tablets will succeed this time around, don't think enterprises will move to the public cloud as much as expected, and am not sure that personalized advertising will always be used to benefit consumers. I do think netbooks are a dead market, mobile devices will become standardized and more like computers, and that 2010 will see big advances in local search and augmented reality on mobile devices.

If you have any thoughts on these predictions or some of your own to add, please comment either here or at blog@CACM.

Update: Another prediction, not in that list, that might be worth including here, "Who Needs Massively Multi-Core?"

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Lectures on Computational Advertising

Slides from all the lectures of Andrei Broder's recent Computational Advertising class at Stanford University now are available online. Andrei is a VP and Chief Scientist at Yahoo and leads their Advertising Technology Group.

Lecture 6 (PDF) is particularly interesting with its coverage of learning to rank. Lecture 8 (PDF) has a tidbit on behavioral advertising and using recommender systems for advertising, but it is very brief. The first few lectures are introductory; don't miss lecture 3 (PDF) if you are new to sponsored search and want a good dive into the issues and techniques.

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