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* A history of what everybody has done. Obviously this is just a cartoon because large numbers of users and interactions with items would be required to build a recommender* Next step will be to predict what a new user might like…
*Bob is the “new user” and getting apple is his history
*Here is where the recommendation engine needs to go to work…Note to trainer: you might see if audience calls out the answer before revealing next slide…
Note to trainer: This is the situation similar to that in which we started, with three users in our history. The difference is that now everybody got a pony. Bob has apple and pony but not a puppy…yet
*Binary matrix is stored sparsely
*Convert by MapReduce into a binary matrixNote to trainer: Whether consider apple to have occurred with self is open question
Old joke: all the world can be divided into 2 categories: Scotch tape and non-Scotch tape… This is a way to think about the co-occurrence
Only important co-occurrence is puppy follows apple
*Take that row of matrix and combine with all the meta data we might have…*Important thing to get from the co-occurrence matrix is this indicator..Cool thing: analogous to what a lot of recommendation engines do*This row forms the indicator field in a Solr document containing meta-data (you do NOT have to build a separate index for the indicators)Find the useful co-occurrence and get rid of the rest. Sparsify and get the anomalous co-occurrence
Note to trainer: take a little time to explore this here and on the next couple of slides. Details enlarged on next slide
*This indicator field is where the output of the Mahout recommendation engine are stored (the row from the indicator matrix that identified significant or interesting co-occurrence. *Keep in mind that this recommendation indicator data is added to the same original document in the Solr index that contains meta data for the item in question
This is a diagnostics window in the LucidWorksSolr index (not the web interface a user would see). It’s a way for the developer to do a rough evaluation (laugh test) of the choices offered by the recommendation engine.In other words, do these indicator artists represented by their indicator Id make reasonable recommendations Note to trainer: artist 303 happens to be The Beatles. Is that a good match for Chuck Berry?