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From Matthias Böhmer <matth...@m-boehmer.de>
Subject Re: "Context-aware" recommendations
Date Tue, 12 Oct 2010 20:48:41 GMT
Hi there,

I have been to ACM RecSys and presented a paper at the workshop on
context-aware recommender systems about appazaar, a context-aware
recommender system that suggests apps for the Android phone (please
drop me an email if you are interested in that topic, you can find the
app on the market).

Using the IDRescorer basically follows a post-filtering approach --
i.e. applying context-information after e.g. a collaborative filter.
The method of adding extra dimensions for context mentioned by Steven
--- who by the way also gave an interesting talk in the CARS workshop
;-)  --- follows a model-based approach, where you integrate
context-information into the model itself (see the chapter on
Context-aware Recommender Systems on the forthcoming Recommender
Systems Handbook for a discussion on contextual pre-filtering,
contextual post-filtering, and contextual modelling approaches). There
was an interesting paper presented by Karatzoglou et al. on
"Multiverse recommendation: n-dimensional tensor factorization for
context-aware collaborative filtering". I would really like to see
this algorithms implemented in Mahout. Context-awareness is a really
important topic for recommender systems.

Best,
Matthias

2010/10/12 Steven Bourke <sbourke@gmail.com>:
> Hi - There was an entire track dedicated to context recommender systems at
> this years ACM Recommender Systems, including a challenge using the movie
> pilot dataset
>
> There is a substantial amount of research out there about using contextual
> features in a recommender system. A pretty mainstream approach is to add an
> extra dimension to your matrix when calculating recommender scores. If you
> pop onto scholar.google.com and check out context recommenders or tensor
> matrix you'll find some useful literature. Just adding simple rescore values
> would probably make these additional attributes fall into the domain of
> context based recommenders.
>
> On Tue, Oct 12, 2010 at 12:04 PM, Sean Owen <srowen@gmail.com> wrote:
>
>> Yeah this is embodied in the "IDRescorer" class which lets you
>> influence the final recommendations however you want, for just this
>> sort of reason.
>>
>> On Tue, Oct 12, 2010 at 11:55 AM, Sebastian Schelter
>> <ssc.open@googlemail.com> wrote:
>> > Hi everyone,
>> >
>> > I have some non-release-related offtopic questions ;)
>> >
>> > I've attended an interesting talk last week given by the CTO of
>> > moviepilot.de (a German movie recommendation platform). The talk
>> included a
>> > concept which he called "context-aware" recommendations, which means that
>> > you not only recommend items that a user might like, but you also factor
>> in
>> > the users current context. The example in the talk was that a romantic
>> > evening needs other movie recommendations than an evening with a couple
>> of
>> > guys drinking beer.
>> >
>> > I found this concept very appealing and I thought about whether and how
>> this
>> > could be accomplished with our current recommender framework.
>> >
>> > My idea would be to define some content-related rules like "a movie
>> tagged
>> > with the category action is not suited for the context romantic evening"
>> and
>> > use these rules to create some kind of context-aware Rescorer that only
>> > selects items matching the rules from all recommended items.
>> >
>> > Would this be a viable approach?
>> >
>> > --sebastian
>> >
>>
>

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