intelligent information retrieval based on simple implicit learning
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Intelligent information retrieval based on simple implicit learning
Dr Gavin [email protected]
17 Feb 2016
Imagine we have some content in a database.
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A user runs a query …
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which finds some results …
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and returns them, sorted in some way.
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The user chooses some of them ...
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which adds to our existing click data.
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When we use this data to help sort results ...
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users can learn from each other ...
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and returns can converge on a consensus.
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We can take this simple idea a step further.
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A user’s clicks happen in a sequence ...
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connecting things intelligently as they make choices.
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Integrating over queries, sessions, users, time etc ...
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maps out the connections users are moving along.
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So when we run our query ...
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we can identify dynamically connected secondary items ...
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and use them to modify or extend our returns …
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or to make intelligent recommendations ...
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or to put ads in the right place at the right time etc.
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These returns get a click response ...
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which adds to our existing click data ...
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so the next time our query runs ...
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new things have been learned.
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We don’t need to actually map all this complexity …
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just query relevant click data at query time ...
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but the effect is intelligent query returns …
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that share the right things in the right places …
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and learn continuously via an implicit feedback loop.
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No need for machine learning, complex processing or masses of metadata.
To see how this behaves on real data, take a look athttp://www.slideshare.net/pontneo/quick-introduction-to-the-clickthrough-filter