1) Using the user history for the current user in a more-like-this query against the item-item similarity matrix will produce a user-history based recommendation. Simply fetching the item-item history row for a particular item will give you the item-similarity based recs with no account of user history. One could imagine a user-user similarity setup, but that's not what we did.
2) What you are doing is something else that I was calling a shopping-cart recommender. You are using the item-set in the current cart and finding similar, what, items? A different way to tackle this is to store all other shopping carts then use the current cart contents as a more-like-this query against past carts. This will give you items-purchased-together by other users. If you have enough carts it might give even better results. In any case they will be different. https://github.com/pferrel/solr-recommender But if you already have the item-item similarity matrix indexed this project wont add much. If you have purchase events and view-details events IDed by user you might try out the cross-recommender part. We've been searching for a data set to try this on. On Oct 9, 2013, at 12:54 PM, Michael Sokolov <msoko...@safaribooksonline.com> wrote: On 10/9/13 3:08 PM, Pat Ferrel wrote: > Solr uses cosine similarity for it's queries. The implementation on github > uses Mahout LLR for calculating the item-item similarity matrix but when you > do the more-like-this query at runtime Solr uses cosine. This can be fixed in > Solr, not sure how much work. It's not clear to me whether it's worth "fixing" this or not. It would certainly complicate scoring calculations when mixing with traditional search terms. > > It sounds like you are doing item-item similarities for recommendations, not > actually calculating user-history based recs, is that true? Yes that's true so far. Our recommender system has the ability to provide recs based on user history, but we have not deployed this in our app yet. My plan was simply to query based on all the items in the user's "basket" - not sure that this would require a different back end? We're not at the moment considering user-user similarity measures. > > You bring up a point that we're finding. I'm not so sure we need or want a > recommender query API that is separate from the Solr query API. What we are > doing on our demo site is putting the output of the Solr-recommender where > Solr can index it. Our web app framework then allows very flexible queries > against Solr, using simple user history, producing the typical user-history > based recommendations, or mixing/boosting based on metadata or contextual > data. If we leave the recommender query API in Solr we get web app framework > integration for free. > > Another point is where the data is stored for the running system. If we allow > Solr to index from any storage service that it supports then we also get free > integration with most any web app framework and storage service. For the demo > site we put the data in a DB and have Solr index it from there. We also store > the user history and metadata there. This is supported by most web app > frameworks out of the box. You could go a different route and use almost any > storage system/file system/content format since Solr supports a wide variety. > > Given a fully flexible Solr standard query and indexing scheme all you need > do is tweak the query or data source a bit and you have an item-set > recommender (shopping cart) or a contextual recommender (for example boost > recs from a category) or a pure metadata/content based recommender. > > If the query and storage is left to Solr+web app framework then the github > version is complete if not done. Solr still needs LLR in the more-like-this > queries. Term weights to encode strength scores would also be nice and I > agree that both of these could use some work. I would like to take a look at that version - I may have missed some discussion about it; would you posting a link please? > > BTW lest we forget this does not imply the Solr-recommender is better than > Myrrix or the Mahout-only recommenders. There needs to be some careful > comparison of results. Michael, did you do offline or A/B tests during your > implementation? I ran some offline tests using our historical data, but I don't have a lot of faith in these beyond the fact they indicate we didn't make any obvious implementation errors. We haven't attempted A/B testing yet since our site is so new, and we need to get a meaningful baseline going and sort out a lot of other more pressing issues on the site - recommendations are only one piece, albeit an important one. Actually there was an interesting idea for an article posted recently about the difficulty of comparing results across systems in this field: http://www.docear.org/2013/09/23/research-paper-recommender-system-evaluation-a-quantitative-literature-survey/ but that's no excuse not to do better. I'll certainly share when I know more :) -Mike > > On Oct 9, 2013, at 6:13 AM, Michael Sokolov <msoko...@safaribooksonline.com> > wrote: > > Just to add a note of encouragement for the idea of better integration > between Mahout and Solr: > > On safariflow.com, we've recently converted our recommender, which computes > similarity scores w/Mahout, from storing scores and running queries > w/Postgres, to doing all that in Solr. It's been a big improvement, both in > terms of indexing speed, and more importantly, the flexibility of the queries > we can write. I believe that having scoring built in to the query engine is > a key feature for recommendations. More and more I am coming to believe that > recommendation should just be considered as another facet of search: as one > among many variables the system may take into account when presenting > relevant information to the user. In our system, we still clearly separate > search from recommendations, and we probably will always do that to some > extent, but I think we will start to blend the queries more so that there > will be essentially a continuum of query options including more or less "user > preference" data. > > I think what I'm talking about may be a bit different than what Pat is > describing (in implementation terms), since we do LLR calculations off-line > in Mahout and then bulk load them into Solr. We took one of Ted's earlier > suggestions to heart, and simply ignored the actual numeric scores: we index > the top N similar items for each item. Later we may incorporate numeric > scores in Solr as term weights. If people are looking for things to do :) I > think that would be a great software contribution that could spur this effort > onward since it's difficult to accomplish right now given the Solr/Lucene > indexing interfaces, but is already supported by the underlying data model > and query engine. > > > -Mike > > On 10/2/13 12:19 PM, Pat Ferrel wrote: >> Excellent. From Ellen's description the first Music use may be an implicit >> preference based recommender using synthetic data? I'm quickly discovering >> how flexible Solr use is in many of these cases. >> >> Here's another use you may have thought of: >> >> Shopping cart recommenders, as goes the intuition, are best modeled as >> recommending from similar item-sets. If you store all shopping carts as your >> training data (play lists, watch lists etc.) then as a user adds things to >> their cart you query for the most similar past carts. Combine the results >> intelligently and you'll have an item set recommender. Solr is built to do >> this item-set similarity. We tried to do this for a ecom site with pure >> Mahout but the similarity calc in real time stymied us. We knew we'd need >> Solr but couldn't devote the resources to spin it up. >> >> On the Con-side Solr has a lot of stuff you have to work around. It also >> does not have the ideal similarity measure for many uses (cosine is ok but >> llr would probably be better). You don't want stop word filtering, stemming, >> white space based tokenizing or n-grams. You would like explicit weighting. >> A good thing about Solr is how well it integrates with virtually any doc >> store independent of the indexing and query. A bit of an oval peg for a >> round hole. >> >> It looks like the similarity code is replaceable if not pluggable. Much of >> the rest could be trimmed away by config or adherence to conventions I >> suspect. In the demo site I'm working on I've had to adopt some slightly >> hacky conventions that I'll describe some day. >> >> On Oct 1, 2013, at 10:38 PM, Ted Dunning <ted.dunn...@gmail.com> wrote: >> >> >> Pat, >> >> Ellen and some folks in Britain have been working with some data I produced >> from synthetic music fans. >> >> >> On Tue, Oct 1, 2013 at 2:22 PM, Pat Ferrel <p...@occamsmachete.com> wrote: >> Hi Ellen, >> >> >> On Oct 1, 2013, at 12:38 PM, Ted Dunning <ted.dunn...@gmail.com> wrote: >> >> >> As requested, >> >> Pat, meet Ellen. >> >> Ellen, meet Pat. >> >> >> >> >> On Tue, Oct 1, 2013 at 8:46 AM, Pat Ferrel <pat.fer...@gmail.com> wrote: >> Tunneling (rat-holing?) into the cross-recommender and Solr+Mahout version. >> >> Things to note: >> 1) The pure Mahout XRecommenderJob needs a cross-LLR or a cross-similairty >> job. Currently there is only cooccurrence for sparsification, which is far >> from optimal. This might take the form of a cross RSJ with two DRMs as >> input. I can't commit to this but would commit to adding it to the >> XRecommenderJob. >> 2) output to Solr needs a lot of options implemented and tested. The >> hand-run test should be made into some junits. I'm slowly doing this. >> 3) the Solr query API is unimplemented unless someone else is working on >> that. I'm building one in a demo site but it looks to me like a static >> recommender API is not going to be all that useful and maybe a document >> describing how to do it with the Solr query interface would be best, >> especially for a first step. The reasoning here is that it is so tempting to >> mix in metadata to the recommendation query that a static API is not so >> obvious. For the demo site the recommender API will be prototyped in a bunch >> of ways using models and controllers in Rails. If I'm the one to do the a >> Java Solr-recommender query API it will be after experimenting a bit. >> >> Can someone introduce me to Ellen and Tim? >> >> On Sep 28, 2013, at 10:59 AM, Ted Dunning <ted.dunn...@gmail.com> wrote: >> >> The one large-ish feature that I think would find general use would be a >> high performance classifier trainer. >> >> Flor cleanup sort of thing it would be good to fully integrate the streaming >> k-means into the normal clustering commands while revamping the command line >> API. >> >> Dmitriy's recent scala work would help quite a bit before 1.0. Not sure it >> can make 0.9. >> >> For recommendations, I think that the demo system that pat started with the >> elaborations by Ellen an Tim would be very good to have. >> >> I would be happy to collaborate with somebody on these but am not at all >> likely to have time to actually do them end to end. >> >> Sent from my iPhone >> >> On Sep 28, 2013, at 12:40, Grant Ingersoll <gsing...@apache.org> wrote: >> >>> Moving closer to 1.0, removing cruft, etc. Do we have any more major >>> features planned for 1.0? I think we said during 0.8 that we would try to >>> follow pretty quickly w/ another release. >>> >>> -Grant >>> >>> On Sep 28, 2013, at 12:33 PM, Ted Dunning <ted.dunn...@gmail.com> wrote: >>> >>>> Sounds right in principle but perhaps a bit soon. >>>> >>>> What would define the release? >>>> >>>> Sent from my iPhone >>>> >>>> On Sep 27, 2013, at 7:48, Grant Ingersoll <gsing...@apache.org> wrote: >>>> >>>>> Anyone interested in thinking about 0.9 in the early Nov. time frame? >>>>> >>>>> -Grant >>> -------------------------------------------- >>> Grant Ingersoll | @gsingers >>> http://www.lucidworks.com >>> >>> >>> >>> >>> >> >> >> >> >
