Agree. I have not seen this patent. I base my work on public research and academic papers. I have no reason to believe there are any patent issues here.
On 9/30/08, Grant Ingersoll <[EMAIL PROTECTED]> wrote: > I adhere to Doug's philosophy on patents, and refuse to look at them > or do searches for them. I am not a lawyer, nor a judge, nor a patent > officer, and am thus completely unqualified to even begin to venture > an opinion. > > Sorry, > Grant > > > On Sep 30, 2008, at 1:09 PM, Otis Gospodnetic wrote: > >> Hello, >> >> Thanks for the pointers, Grant. Regarding that Amazon item-item >> recommendation. It looks like that's patented: >> >> http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=amazon.ASNM.&OS=AN/(amazon)&RS=AN/amazon >> >> Does that mean one cannot implement this in Taste (or any other >> piece of software)? Even if used in non-shopping purposes? >> >> >> Thanks, >> Otis >> >> >> ----- Original Message ---- >>> From: Grant Ingersoll <[EMAIL PROTECTED]> >>> To: [email protected] >>> Sent: Monday, September 29, 2008 9:43:58 AM >>> Subject: Re: Recommending when working with binary data sets >>> >>> Not sure I know the answer in terms of Taste, but did a little bit of >>> digging (mind you, I'm no CF expert, but I'm learning thanks to Taste >>> and Sean). >>> >>> At any rate, came across: >>> Started at Wikipedia's page: >>> http://en.wikipedia.org/wiki/Collaborative_filtering >>> >>> which lead to http://en.wikipedia.org/wiki/Slope_One, which then has >>> an interesting comment about Amazon's item-item approach, which, via >>> Google Scholar leads to: >>> >>> http://dsonline.computer.org/portal/site/dsonline/menuitem.9ed3d9924aeb0dcd82ccc6716bbe36ec/index.jsp?&pName=dso_level1&path=dsonline/2003_Archives/0301/d&file=wp1lind.xml&xsl=article.xsl&;jsessionid=LghY1grHgYJpBTLpWjX5NtvQwhH1Bkv9rpfXT4VnpVtDNVpfZ8n0!-1404507079 >>> >>> In particular, see the "How it Works" section. Essentially, it >>> describes how they build the item to item similarity matrix, which I >>> believe is also what you need to do. >>> >>> HTH, >>> Grant >>> >>> On Sep 26, 2008, at 1:52 PM, Otis Gospodnetic wrote: >>> >>>> Hi, >>>> >>>> I've been reading the chapter on recommendations in Programming >>>> Collective Intelligence and looking at Taste. The examples in PCI >>>> all assume scenarios where items to recommend have been rated by >>>> users on some scale. I understand how items can be recommended to >>>> users using item-based filtering and user-item ratings and why this >>>> is preferred over user-based filtering when the number of users is >>>> larger than the number of items. >>>> But what if all I've got is item-item similarity (content-based) and >>>> there are no user-item ratings? Say I have a situation where people >>>> simply either consume content (e.g. read an article, watch a >>>> video...) or not consume it (don't read an article, don't watch the >>>> video...). In other words, I really have only yes/no or 1/0 or >>>> seen/ >>>> not seen type "rating". >>>> >>>> I can't really use Euclidean distance or Pearson correlation >>>> coefficient, can I? >>>> >>>> What do people use in such scenarios? Would it make sense to use >>> http://en.wikipedia.org/wiki/Jaccard_index >>>> for such cases? >>>> ... Ah, I do see javadoc in TanimotoCoefficientSimilarity saying >>>> exactly that, good. >>>> >>>> But then my question is: >>>> Doesn't the use of Jaccard/Tanimoto mean going back to the expensive >>>> user-user similarity computation? >>>> >>>> That is, if I need to recommend items for user U1 don't I need to: >>>> 1) have user-user similarity pre-computed (and recomputed >>>> periodically) >>>> 2) find top N users U{2,3,4,...N} who are the most similar to U1 >>>> 3) then for these top N most similar users find their "seen" items >>>> that U1 has not seen (possibly limit this to only recently seen >>>> items) >>>> 4) select top N items from 3) and recommend those to U1. >>>> >>>> If so, then 1) is again expensive. >>>> And what how would one go about selecting top N items from the list >>>> in this case other than ordering them by user-user similarity? >>>> >>>> Of course, something is telling me I'm demonstrating that I don't >>>> yet have the full grasp of item-based filtering. I hope that's the >>>> case! :) >>>> >>>> Thanks, >>>> Otis >>> >>> >
