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From Pat Ferrel <>
Subject Re: Setting up a recommender
Date Thu, 25 Jul 2013 03:32:07 GMT
Understood, catalog categories, tags, etc will make good metadata to be included in the query
and putting in separate fields allows us to separately boost each in the query. UserIDs that
have interacted with the item is an interesting idea.

However the specific case I'm describing is not about content similarity. Talking here about
item-item similarity exactly as encoded in the similarity matrix. The order or rank of these
item-item similarities should be preserved and I was proposing doing so with the order of
the itemID terms in the document.

The query will return history based recs ranked by the order Solr applies. The doc itself
for any item contains similar items ordered by their similarity magnitude, precalculated in
Mahout RowSimilarityJob.

On Jul 24, 2013, at 7:19 PM, Ted Dunning <> wrote:

Content based item similarity is a fine thing to include in a separate field.  

In addition, it is reasonable to describe a person's history in terms of the meta-data on
the items they have interacted with.  That allows you to build a set of socially driven meta-data
indicators as well.  This can be useful in the restaurant example where you might find that
"elegant" or "home-style" might be good indicators for different restaurants even if those
terms don't appear in a restaurant description.  

Sent from my iPhone

On Jul 23, 2013, at 18:26, Pat Ferrel <> wrote:

> Honestly not trying to make this more complicated but…
> In the purely Mahout cross-recommender we got a ranked list of similar items for any
item so we could combine personal history-based recs with non-personalized item similarity-based
recs wherever we had an item context. In a past ecom case the item similarity recs were quite
useful when a user was looking at an item already. In that case even if the user was unknown
we could make item similarity-based recs.
> How about if we order the items in the doc by rank in the existing fields since they
are just text? Then we would do user-history-based queries on the fields for recs and docs[itemID].field
to get the ordered list of items out of any doc. Doing an ensemble would require weights though.
Unless someone knows a rank based method for combining results. I guess you could vote or
add rank numbers of like items or the log thereof...
> I assume the combination of results from [B'B] and [B'A] will be a query over both fields
with some boost or other to handle ensemble weighting. But if you want to add item similarity
recs another method must be employed, no?
> From past experience I strongly suspect item similarity rank is not something we want
to lose so unless someone has a better idea I'll just order the IDs in the fields and call
it good for now.

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