| Description |
1 online resource (1 streaming video file (34 min., 41 sec.)) |
| Summary |
"In its raw state, web browsing data is both too detailed and too sparse to be comprehensible, let alone actionable. Melinda Han Williams (Dstillery) explores semantic embeddings as a novel approach for understanding observed digital consumer behavior and details how to use a semantic embedding of web browsing behavior to drive unsupervised clustering for customer segmentation. You'll learn how Dstillery has trained a neural network on 15 billion behavioral interactions. The resulting model can be seen as a much lower dimensional embedding of the internet and, if projected into two or three dimensions, as an interactive map. This taxonomy of internet behavior can be used as the foundation for a number of applications, providing unparalleled insights into consumer behavior and needs. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco."--Resource description page |
| Notes |
Title from title screen (viewed January 10, 2020) |
| Performer |
Presenter, Melinda Han Williams |
| SUBJECT |
Strata Conference (2019 : San Francisco, Calif.)
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| Subject |
Artificial intelligence.
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Machine learning.
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Marketing -- Management -- Data processing.
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Consumer behavior.
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Big data.
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Artificial Intelligence
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artificial intelligence.
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Artificial intelligence.
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Big data.
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Consumer behavior.
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Machine learning.
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Marketing -- Management -- Data processing.
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| Form |
Streaming video
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| Author |
O'Reilly & Associates, publisher.
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