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From PierLorenzo Bianchini <>
Subject Re: adjusted cosine similarity for item-based recommender?
Date Fri, 03 Apr 2015 17:25:19 GMT
thank you for your answers. I understand now why there is no built-in "adjusted cosine similarity"
Unfortunately though, my question still stands - if I had to implement an "adjusted cosine
similarity", how would I do it?
For sake of simplicity I'll move the discussion to my other question "fast performance way
of writing preferences to file?" and I'll explain there why I have to do it (or at least I
*think* I have to do it).
Looking forward to hearing from you in my other discussion, and thank you again (I'll reply
on the other thread in a few minutes).

Pier Lorenzo

On Fri, 4/3/15, Pat Ferrel <> wrote:

 Subject: Re: adjusted cosine similarity for item-based recommender?
 To: "" <>
 Date: Friday, April 3, 2015, 6:05 PM
 I’d add that overview
 and references are here:
 There are many benefits to
 this architecture including being able to make recs to
 anonymous users (as long as you have a little history for
 them) no need to retrain the recommender. The server is a
 fast scalable search engine and so the multimodal part is
 accessed by changing the query, which also means you can use
 context in realtime. Like what category you want recs to
 favor or be filtered by. 
 In a cooccurrence recommender ratings are
 ignored. You want to gather history of some user action that
 is as close to the intended action you want to recommend.
 For ecom that’s probably purchase, for movies it might be
 a full movie view. The quality of this action is important.
 For an example movie
 recommender check the demo app, which uses most of the
 techniques mentioned in the references; 
 On Apr 3, 2015, at 8:48 AM,
 Ted Dunning <>
 For practical
 recommendation systems, ratings are almost irrelevant.
 Ratings were prominent in the original academic
 work on recommendations
 largely because with
 the early research systems, users had no recordable
 interactions with content other than ratings. 
 The Taste component of
 Mahout was written
 largely by following that literature.
 In fact, in a real world system, this turns out
 to be very wrong.  Ratings
 are a very poor
 source of recommendation information in most real
 applications for two reasons:
 1) interpretation is hard
 (your issues with bias are just the beginning)
 2) volume is very low because
 most (often >>99%) users don't rate
 If you are building a
 production recommender you should be looking at
 indicator-based techniques and moving to
 multi-modal recommendations.
 The code in question was deprecated precisely
 because it has little
 practical impact.
 On Fri, Apr 3, 2015 at 3:36 AM, PierLorenzo
 Bianchini <>
 > hello
 > I'm trying to build
 item-based recommender that would take the users'
 > rating-bias into account. According to my
 class' slides, that should
 > involve
 "adjusted cosine similarity". I couldn't find
 such implementation
 > in Mahout... did I
 search it in the wrong place or should I implement it?
 > If so, how? Any tips would be welcome
 since I'm new to Mahout...
 > I found out that in mahout-0.8 there was a
 (deprecated) class that seemed
 > to do
 what I'm looking for (
 > Does anybody know why this was removed?
 > Note: I also posted
 the question on stackoferlow (
 > )
 > Thank you in
 advance! Regards,
 Pier Lorenzo

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