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From Lance Norskog <goks...@gmail.com>
Subject Re: Hybrid RecSys — ways to do it
Date Tue, 08 Feb 2011 06:28:35 GMT
Combining various recommender algorithms is called "stacking". All of
the Netflix contest winners and runner-ups used 25-100 different
recommendation algorithms with finely tuned weights.

On Mon, Feb 7, 2011 at 6:27 PM, Ted Dunning <ted.dunning@gmail.com> wrote:
> See also here http://arxiv.org/abs/1006.2156
>
> Another approach is to build a conventional recommender for items and attach
> an indicator of how much information that recommender has to work with
> (number of occurrences of the recommended item might be good enough).  Then
> do the same for some prominent characteristic of the items.  This might give
> you a "brand" recommender for retail products or an "artist" recommender for
> music.   For this more generic recommender, you might be able to directly
> use the counts from the user's history.  Finally, build "top-40" models for
> overall item, brand, artist or what have you characteristics.
>
> Now train a simple model to combine these results to find items that the
> user is likely to engage with.  SGD is an easy choice here.  At
> recommendation time, you would run all of the constituent recommenders and
> use the SGD model to rescore the union of their results.
>
> If done well, the brand and top-40 models will give you decent cold start
> behavior while the real collaborative filtering models will give you good
> performance after the cold-start.  The SGD should be able to meld these
> values well if it has a good indicator of how reliable each sub-model is.
>
> On Mon, Feb 7, 2011 at 4:11 PM, Steven Bourke <sbourke@gmail.com> wrote:
>
>> Check http://www.springerlink.com/content/n881136032u8k111/ out. Do a
>> search
>> on google scholar and you might find  the pdf.
>>
>> What type of data / recommendations are you trying to make? Standard
>> collaborative filtering techniques arent a bad thing.
>>
>> On Tue, Feb 8, 2011 at 12:05 AM, Chris Schilling <chris@cellixis.com>
>> wrote:
>>
>> > I am interested in this problem as well (combining content similarity
>> with
>> > CF).
>> >
>> > I want to build a system which makes use of the CF part of Mahout:  I am
>> > recommending products to users.  Along with user ratings/preferences for
>> > products, I also have a content based similarity metric calculated for
>> each
>> > item-item pair.
>> >
>> > I do not have a lot of experience in producing "hybrid" recommendations.
>> >  Do you generally think the most appropriate thing to do is to boost
>> > recommendations from CF?  Or do you like the 2nd method of using a custom
>> > item similarity to combine cf similarity with content similarity?  It
>> seems
>> > straight forward enough to try both, just trying to get a feel for how to
>> > approach this.
>> >
>> > Can you recommend any papers describing combination of content and CF?
>> >
>> > Thanks for your help!
>> > Chris S.
>> >
>> > On Feb 7, 2011, at 9:50 AM, Sebastian Schelter wrote:
>> >
>> > > Hi Alexandre,
>> > >
>> > > I dont think there is "one golden way" but I can give you some hints
>> > where to start regarding itembased recommenders. I think there are three
>> > points where you could customize the behavior to enable "hybrid"
>> > recommendations:
>> > >
>> > > * you can use a custom Rescorer to either filter the resulting
>> > recommended items (e.g. restrict the result to a certain type/category of
>> > items) or to boost some of them (e.g. by looking at their content)
>> > >
>> > > * you can use a custom ItemSimilarity which could compute a blended
>> score
>> > by combining the usual similarity score with an additional contentbased
>> > similarity score
>> > >
>> > > * as collaborative filtering usually suffers from the "cold-start
>> > problem" (you cannot make any assumptions about new users or items until
>> > you've seen some interactions), you could work around this by
>> implementing a
>> > custom CandidateItemsStrategy/MostSimilarItemsCandidateItemsStrategy that
>> > uses content properties to find items to recommend if the user or the
>> item
>> > is new
>> > >
>> > >
>> > > --sebastian
>> > >
>> > > On 07.02.2011 16:56, Alexandre Rodrigues (FEUP) wrote:
>> > >> Hello Mahouters out there!
>> > >>
>> > >> I'm diving into the amazing world of Mahout and Hadoop and I have some
>> > >> questions about it. My project consists in developing a recommender
>> > system
>> > >> for TV shows, and my objective is to study how can I ensemble/mix some
>> > >> approaches, like content-based and collaborative filtering (with
>> weights
>> > for
>> > >> example). Is there _the way_ to do it using Mahout, or it's an
>> > unexplored
>> > >> subject at the moment?
>> > >>
>> > >> Thanks in advance!
>> > >> --
>> > >> Alexandre Rodrigues
>> > >>
>> > >
>> >
>> >
>>
>



-- 
Lance Norskog
goksron@gmail.com

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