Analytics Strategist

December 18, 2008

Google’s achilles’ heel – a follow up

Filed under: Datarology, Technology — Tags: , , , , — Huayin Wang @ 9:52 pm

Follow up to one of my early post about google’s achillis’ heel, I’d like to add that Google’s latest searchwiki seems to be an interesting response to what I mentioned earlier — I know I know it is not quite like that 🙂

I love to count the many different ways of ranking stuff in response to a search query.  The objects, the stuffs, can be text, link, document, image, video etc..  The ranking principle is the essentially a rule of relevance and/or similarity. I count four main types:

1) by content similarity, the algorithm could be PageRank, HITS etc..  For images and videos, this can prove to be very difficult because it involves not only the hard core technology such as pattern recognization for images, but also involves large stocks of prior knowledge about object categorization etc..

2) by similarity of user behavior, when applied some kinds of collective intelligence, or collaborative filtering type of algorithms.  User behavior can serve as implicit voting; with algorithms’ help, the complexity of the ranking operation can be dramatically reduced.

3) by similarity of user explicit ratings.  Users’ search phrase and explicit ratings ( ratings/reviews on amazon, as well as Google’s latest searchWiki, which interestingly only affect what user see next time, not anyone else’).  Some types of social/collective intelligence algorithm has to be applied in order to solve the complexity issue, as well as the sparse data problem associated it when crossing search query with user ratings.

4) of course, there is always the money logic.

If you know more ranking logic than what posted here, I’d like to know it ..

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July 29, 2008

Google’s achilles’ heel

Filed under: Advertising, business strategy, Datarology, Technology, Uncategorized — Tags: — Huayin Wang @ 5:22 pm

just a thought 🙂
There have been three search engine ranking principles at works: 1) by content match with search query, 2) by user feedback (or social search) data to query or similar query, and 3) by bidding price. The logic that used by Google Adwords is a complex combination of all three (relevancy, CTR and bid price).

For example, Amazon and Netflex represent the pure form of 2).

All three principles have their own merit and, here’s why it is important, many times one pure logic may match users’ intent better than a complicated mix.

Google’s ranking logic for Adwords evolved overtime, keeping a careful balance so far. But how far can it goes? Will a dynamic logic that mixes the three in significantly different way be a disruptive technology one day?

Your thoughts?

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