4. Oh Yes! Congratulations Season-to-date online spend: $ 13B UP 15% Black Friday Online spend: $816MM UP 26% Thanksgiving Day Online Sales: $479MM UP 18%
5. Today’s Menu Topic modeling Vector space models Latent Dirichlet Allocation & the SERPs Tactical Advice
11. What Topic Modeling Does Topic modeling uses contextual clues to connect words w/similar meanings and distinguish between uses of words w/multiple meanings. http://www.stanford.edu/~kaisa/research.html
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The search engine can use TF*IDF to determine that “ Wiggum ” is a much less common word than “ chief ” and thus, Content A is more relevant to the query than Content B. NOTE: This example also does a good job of showing the inherent weakness of a metric like keyword density.
As humans reading both sentences, we can infer that Content B is obviously about the musical instrument – a piano – and the woman playing it. But a search engine armed with only the methods we described above will struggle since both sentences use the words “keys” and “notes”, some of the few clues to the puzzle.
Term vector spaces, topic modeling and cosine similarity sound like a tough concepts. However, Ben (along with Rand Fishkin and Will Critchlow , whose Cambridge mathematics degree came in handy) helped explain these issues. I'll do my best to replicate that here:
In this imaginary example, every word in the English language is related to either &quot; cat &quot; or &quot; dog “. They are the only topics available. To measure whether a word is more related to &quot;dog,&quot; we use a vector space model that displays those relationships mathematically. The illustration does a reasonable job showing our simplistic world. Words like &quot;bigfoot&quot; are perfectly in the middle with no more closeness to &quot;cat&quot; than &quot;dog.&quot; But words like &quot;canine&quot; and &quot;feline&quot; are clearly closer to one that the other and the degree of the angle in the vector model illustrates this- and gives us a number. BTW, in an LDA vector space model, topics wouldn't have exact label associations like &quot;dog&quot; and &quot;cat&quot; but would instead be things like &quot;the vector around the topic of dogs.“ Taking the simple model above and scaling it to thousands or millions of topics, each of which would have its own dimension. Using this construct, the model can compute the similarity between any word or groups of words and the topics its created. You can learn more about this from Stanford University's posting of Introduction to Information Retrieval , <http://nlp.stanford.edu/IR-book/html/htmledition/irbook.html> which has a specific section on Vector Space Models <http://nlp.stanford.edu/IR-book/html/htmledition/dot-products-1.html>
Correlation of our LDA Results w/ Google.com Rankings The SEOmoz team have put together a topic modeling system based on a relatively simple implementation of LDA.
Vertical bars indicate Standard Error in the diagram. SE is relatively low thanks to a large sample set.
Are good links are more likely to point more &quot;relevant” pages? Do other aspects of Google's algorithm naturally bias toward these results? Correlation is NOT causation!
Let’s review some of the basic tenets that Google reps continue to stress to search marketers – once you’ve chosen a keyword with the highest value / lowest competition quotient, it takes more than simple frequency of use to rank well for that word.
The recent Panda updates addressed a number of spam issues in an attempt to provide a better result to searchers.
This snippet of an episode from Alan Sorkin’s The West Wing exquisitely demonstrates what I’d love to convey.
The variety of places from which we get traffic today helps us segment our audience by interest, online behavioral habits, and comfort zones. People who spend a great deal of time in Facebook are comfortable with its interface and layout. If you’re creating content for a landing page for folks coming from Facebook, consider a layout and post(s) that will be a good segue between their Facebook experience and your site. Same goes for Twitter, YouTube, and other social network visitors.
Highly targeted infographics are the new link bait. Because we have an exponentially increasing volume of information to absorb, we need better ways to absorb it faster and more efficiently. Info-graphics aren’t a flash in the pan.
There’s no short cut for making info-graphics go viral. Great graphics make it happen. Invest in a decent info designer and keep him/her close.
Image, news, maps, video… there’s more to search than the general SERPs.
Image, news, maps, video… there’s more to search than the general SERPs.
Image, news, maps, video… there’s more to search than the general SERPs.
Image, news, maps, video… there’s more to search than the general SERPs.
Who needs position 1 when video click through rates exceed that position, even when located in position 3 or 4?
Who needs position 1 when video click through rates exceed that position, even when located in position 3 or 4?
Q&A sites can bring significant direct traffic, establish you as an authority in your field, and invite news media to contact you when they need expert info or opinions.
User generated content can help you create consistently fresh, unique content. Careful moderation and high standards will maintain the quality standards you need.
SEOmoz’ YOUmoz is a place for our community to share info.
Build it and they will come? Not so much! Share on facebook, youtube, twitter, linkedin, and google+.