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Intelligent information retrieval based on simple implicit learning

Dr Gavin Ruddygav@pontneo.com

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