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From Pat Ferrel <...@occamsmachete.com>
Subject Re: Universal Recommender. How to rank items returned by query on three types of indicators?
Date Sun, 05 Feb 2017 17:36:58 GMT
Nice, someone does read the math :-)

Content: The type of personalized “content” indicators talked about in the slides are
not supported by the Universal Recommender and have little value unless you have no collaborative
filtering data. They can theoretically be mixed with other indicators but you have to have
history of the content a user has preferred in some way and that can also be seen as CF data
so that part of the theory has value in only very specific edge cases like personalized news,
where stories mostly do not get enough events to use for CF. If this is your case  we can
talk more. Most people have CF data and so content cannot be used in this way but can as “intrinic”.

Intrinsic: These are things like categories, tags, subjects, even derived indicators like
LDA Topics, or popularity. They are attached to items as metadata. These are supported by
the UR in several ways including boosts and filters. Imagine an ecom use case where a user
is looking at a piece of “clothing”, at the bottom of the page you show “people who
bought this also bought these” but you want only clothing, not the occasional video of electronics
item. The things at the bottom of the page are “item-based” recommendations, not personalized
but could also be personalized—no matter. The point is that of all recommendations you want
to show only items that have the “category”: [“clothing”]. So it you have attached
this “intrinsic” indicator to items you can query for item or user based recs with category:
clothing. You can filter all recommendations out that do not have the category or you can
boost items that have the category, both are done by changing the “bias” value in the
query. See this page: http://actionml.com/docs/ur_queries <http://actionml.com/docs/ur_queries>

Collaborative Filtering based indicators. Are based on any action, bit of context, or profile
info that you think may relate to the user’s taste or preferences. These are more correctly
called indicators when they are gathered but they go through a correlation test, that checks
if the individual events appear to correlate with the conversion/primary event. So after the
test we call them correlators and they are attached to items. So CF correlators of several
types may be attached to each item along with the Intrinsic correlators.

The Universal Recommender creates a model of all items with all CF and Intrinsic Correlators
attached in a Lucene Index to all items with correlators. The index allows very fast scalable
KNN queries (using cosine similarity). So when you ask the UR for user-based recommendations
for user-1 we look up the recent events of user-1 and use these to make a KNN query to Lucene
(inside of Elasticsearch) for items that have similar correlators. If you ask for user-based
recommendations but bias or boost clothing by 10, the UR will internally multiply the hit
score for “clothing” by 10 and re-rank all results. This means that “clothing” will
be favored in results but if there are no recs for clothing, other types of recs may be returned.

Scores: These are literally the sum of “dot products” of all indictors with boosts accounted
for. Dot products are sometimes called “cosine” since the cosine of the angle between
two vectors is the dot product of the normalized vectors. Each indicator is a vector, if you
refer back to the slides and the total score is the sum of one vector times the entire matrix.
If you then sum the dot products it is the score for all items. Lucene actually does this
but makes use of special indexing and the sparseness of the data and query. So the result
from Lucene is the items that are K Nearest Neighbors to the indicator vectors in the query.
Conceptually Lucene does this for all items in the index but it skips 99% of them and distributes
queries to produce the answer very quickly. The math in the slides shows what you would get
if you did the matrix math for all data and if you paginated and returned all recommendations
you would get exactly the results in the slides, but all you care about are the top k—therefor
KNN

TLDR; After the model is created with Mahout the last phase of the matrix math, finding the
most similar items done inside Elasticsearch so one query returns the top ranked results.
The scores can be explained (by the math you read) but are of no real use, only the rank matters.


BTW the CCO algorithm in partly implemented in Mahout with the last phase in Elasticsearch,
and you can get community support for the Universal Recommender here: https://groups.google.com/forum/#!forum/actionml-user
<https://groups.google.com/forum/#!forum/actionml-user>


On Feb 5, 2017, at 12:42 AM, Peng Zhang <pzhang.xjtu@gmail.com> wrote:

Hi,

Suppose we have created three types of indicators (coocurrence, content and
intrinsic) and indexed them into Ellastic Search (ES). Then we query on
these three types of indicators of a user to get recommended items. How
does Universal Tecommender rank the items recommended based on these three
types of indicators?

I have gone thru the slides on Universal Recommender created by Pat. It's
very informative. Here is the link:
https://www.slideshare.net/mobile/pferrel/unified-recommender-39986309

Thanks
-Peng


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