mahout-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Haters get Love too
Date Wed, 04 Nov 2015 03:50:37 GMT
No. Not entirely surprising, but it is *really* nice to get some public
results on this.

The treatment of the negatives as a separate cross term instead of just
lumping them together is a very significant difference.


On Tue, Nov 3, 2015 at 3:42 PM, Peter Jaumann <peter.jaumann1@gmail.com>
wrote:

> Fascinating!!! Not too surprising really!!!
> On Nov 3, 2015 6:31 PM, "Suneel Marthi" <smarthi@apache.org> wrote:
>
>> Thanks Pat, very interesting indeed.
>>
>> On Tue, Nov 3, 2015 at 6:20 PM, Pat Ferrel <pat@occamsmachete.com> wrote:
>>
>> > A colleague of mine just build a MAP@k precision evaluator for the
>> Mahout
>> > based cooccurrence recommender we’ve been working on and we ran some
>> data
>> > scraped from rottentomatoes.com <http://rottentomatoes.com/> They have
>> > “fresh” and “rotten” reviews tied to reviewer ids.
>> >
>> > A fair bit of discussion has gone on about how to use negative
>> > preferences. We have been saying that negative preferences might be
>> > predictive of positive preferences and the cross-cooccurrence code in
>> the
>> > new SimilarityAnalysis.cooccurrence method can make the data usable.
>> >
>> > We took the RT data for two “actions”: “fresh" as the primary, the best
>> > indicator of preference, and “rotten” as the secondary indicator. We
>> found
>> > that MAP using only “fresh” was bettered by almost 20% when we included
>> > “rotten” as the secondary cross-cooccorrence action. For the strict out
>> > there we did not directly isolate the two actions, which is work
>> remaining
>> > so some of the lift might be due to just having more data but it’s a
>> really
>> > good first step because more data doesn't always translate to better
>> > performance and anyway it’s data you wouldn’t have otherwise.
>> >
>> > This opens up a new way to compare all sorts of other user signals, some
>> > long considered to be unusable by recommenders. Gender, location,
>> category
>> > preferences are now fair game for testing.
>> >
>> > BTW we used this recommender, which is based on Mahout Samsara’s matrix
>> > math, cooccurrence and LLR.
>> > https://github.com/pferrel/scala-parallel-universal-recommendation <
>> > https://github.com/pferrel/scala-parallel-universal-recommendation>
>>
>

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message