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From: Pat Ferrel <pat.fer...@gmail.com> Subject: Re: Solr recommender Date: April 26, 2014 at 9:07:57 AM PDT To: d...@mahout.apache.org Yes, it already does. It’s not named well, all it really does is create an indicator matrix (item-item similarity using LLR) in a form that is digestible by a text indexer. You could use Solr or ElasticSearch to do the indexing and queries. In the actual installation on the demo site https://guide.finderbots.com the indicator matrix is put into a DB and Solr is used to index the item collection’s similarity data field. The queries are handled by the web app framework. If I swapped out Solr for ElasticSearch for indexing the DB, it would work just fine and I looked into how to integrate it with my web app framework (RoR). The integration methods were significantly different though so I chose not to do both. The reason I chose to put the indicator matrix in the DB is because it makes it very convenient to mix metadata into the recs queries. In the case of the demo site where the items are videos I have a bunch of recommendation types: 1) user-history based reqs—query is recent user “likes” history, the query is on the videos collection specifying the similar items field, which is a list of video id strings. This is most usually what people think a recommender does but is only the start. 2-9 are use various methods of biasing the results by genre metadata. Search engines also allow filtering by fields so you can specify videos filtered by source. So you can get comedies based on your “likes” filtered by source = Netflix. in fact when you set the source filter to Netflix every set of recs will contain only those on Netflix There are so many ways to combine bias with filter and what you use as the query, that putting the fields in a DB made the most sense. I am still thinking of new ways to use this. For instance item-set similarity, which is used to give shopping cart recs in some systems. On the demo site you could do the same with the watchlist if there were enough watchlists. Use the user’s watchlist as query against all otehr watchlists and get back an ordered set of watchlists most similar to yours, take recs from there. Some day I’ll write some blog posts about it but I’d encourage anyone with data to try the DB route rather than raw indexing of the text files just for the amazing flexibility and convenience it brings. On Apr 26, 2014, at 8:25 AM, Saikat Kanjilal <sxk1...@hotmail.com> wrote: Pat, I was wondering if you'd given any thought to genericizing the Solr recommender to work with both Solr and elasticsearch, namely are there pieces of the recommender that could plug into or be lifted above a search engine ( or in the case of elasticsearch a set of rest APIs). I would be very interested in helping out with this. Thoughts? Sent from my iPad
