Jeff dalton's Public Library

The popularity of similar knowledge search products in Taiwan, Korea, China and Japan are already making an impact on people searching the web in those countries. We are starting to see a shift in search technology from a world dominated by algorithms and machines exclusively to search results made better through people.

Quoting a 2005 Kelsey Group report, Geoff tells us that "55% of Internet users use search engines to find info about local firms. The estimated spending on local search was only $162 million. In 2009, it's estimated to be $3,380 million. Since close to 70% of small businesses don't have websites, they can't currently do paid online advertising. Therefore, local search will need some development before it can really take off. Geoff sees the advancement of Pay Per Call and the creation of more rich information such as maps and satellite pictures as key factors for local search because they will help people better evaluate businesses.

They told me that industry researchers are seeing somewhere between a five to 15% productivity gain when someone goes from one monitor to two.

Furthermore, handhelds will truly be digital assistants. They will know location & preferences in order to deliver what you want, when you want and now where you want.

Social communities will become more and more core to interactions and marketing on the web.

Some of the responsibility for fleshing out local search also lies with business owners themselves. I still find myself almost constantly frustrated when I try to check out a local restaurant online. It takes very little time to scan a menu in and create a PDF, yet very few local restaurants have any type of menu online. Even basic information like whether credit cards are accepted, what the price range is and what the level of dress is would go a long way toward satisfying a local searcher, yet few local businesses take the time to make sure that even the most basic data is offered up on the major search engines.

The Undercover Economist in me wonders why anyone would write a review and, given that people do write reviews, why anyone would pay attention to one. Writing a review takes time and effort and appears to offer no reward. At the time of writing, about one in 500 buyers of the American edition of my book had left a review. The other 499 hadn't bothered. This is as economic theory would predict.

That fact has implications for a potential customer trying to interpret the reviews. This is a highly biased sample of reviewers, although it's not clear quite what the bias is: do people review books when they're especially pleased or when they're especially disappointed? Either way, Amazon reviewers are not normal people: one of my reviewers calls himself Drool Clueless - perceptively, I think. Another, Compassionate Conservative, wrote 85 substantial reviews of other books in the 10 days after reviewing mine. That doesn't seem humanly possible, let alone typical.

Location-aware phones could also guide a vacationer to restaurants or other establishments similar to those they have at home by comparing the owner's usual haunts to reviews and recommendations found in a database pertinent to the cell owner's current location.

You mentioned an interest in personalized search here and in your previous post. This paper focused on a profile-based method of personalized search, building a list of your interests, group or individualized personalization vectors for those interests, and using that to bias all of your searches. This is also the approach described by the Kaltix team and used in Google Personalized Search.


The problems with this approach are that it is expensive to compute all the personalization vectors (or vector fragments), the personalization will not adapt quickly to new data or trends, and the personalization has to be fairly coarse-grained to have any chance of being feasible.


The approach where I have focused my attention is using short-term behavior to do fine-grained search personalization. For example, if I do search A, don’t find what I want, then refine that search to search B, the two searches are treated independently. I see the same results for search B as everyone else sees. That is clearly wrong. There is valuable information in what I found or failed to find in search A that should be applied to improve the results in search B.

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