My items are products with name and description and maybe caption extracted from the image too.
2017-05-11 0:12 GMT+04:00 Pat Ferrel <[email protected]>: > What are your items? How much text? What other content? Unless you are > recommending long for blogs or news NLP won’t give you much except maybe > word2vec, which, if it has a good model, will give better than bag-of-words. > > > On May 10, 2017, at 1:05 PM, Marius Rabenarivo <[email protected]> > wrote: > > So in you opinion, do you think that the NLP task should be done in the > Engine part using a library like mallet or should be implemented in > algorithm focused library : mahout? > > 2017-05-10 23:52 GMT+04:00 Pat Ferrel <[email protected]>: > >> That is how to make personalized content-based recommendations.You’d have >> to input content by attaching it to items and recording it separately as a >> usage event per content bit. The input , for instance would be every term >> in the description of an item the user purchased. The input would be huge >> and the current UR + PIO is not optimized for that kind of input. It is not >> a recommended mode to use the UR and is of dubious value without NLP >> techniques such as word2vec or NER instead of bag-of-word type content. It >> might be ok if you have rich metadata like categories or tags. >> >> In general content based recommendations are often little better than >> some filtering of popular or rotating promoted items (with no purchase >> history), both can be done fairly easily with the UR. >> >> Content based with NLP techniques for short lived items like news can >> work well but require extra phases in from of the recommender to do the NLP. >> >> >> >> On May 10, 2017, at 12:33 PM, Marius Rabenarivo < >> [email protected]> wrote: >> >> Hello, >> >> So to what does the matrix T and vector h_t in this slide match to? : >> https://docs.google.com/presentation/d/1MzIGFsATNeAYnLfoR679 >> 7ofcLeFRKSX7KB8GAYNtNPY/edit#slide=id.gf4d43b9e8_1_24 >> >> 2017-05-10 21:10 GMT+04:00 Pat Ferrel <[email protected]>: >> >>> Content based recommendations are based on, well, content. You can >>> really only make recs if you have an example item as with the >>> recommendations you see at the bottom of product page on Amazon. >>> >>> For this make sure t have lots of properties of items, even keywords >>> from descriptions will work, but also categories, tags, brands, price >>> ranges. etc. These all must be encoded as JSON arrays of strings so prices >>> might be one of [“$0-$1”, “$1-$5”, …] other things like descriptions >>> categories or tags can have several strings attached. >>> >>> Then issue an item-based query with itemBias set higher (>1) to make use >>> of usage information first before content since it performs better. Then >>> add query fields for the various properties but include the values of the >>> item referenced in the “item” field. >>> >>> You will get similar items based on usage data unless there is none then >>> content will take over to recommend things with similar content. Play with >>> the itemBias, try >1 by varying amounts since you want usage based >>> similarity over content most of the time you have usage based data in the >>> model. There is no hard rule for the bias. >>> >>> >>> On May 10, 2017, at 6:36 AM, Dennis Honders <[email protected]> >>> wrote: >>> >>> According to the docs, the UR is considered as hybrid collaborative >>> filtering / content-based filtering. >>> In my case I have a purchase history. Quite a lot of products are never >>> bought so traditional techniques won't be able to make recommendations. For >>> those products (never bought/sold), will recommendations be made with >>> content-based filtering techniques? >>> If so, what techniques are used in UR? >>> >>> 2017-05-08 19:02 GMT+02:00 Pat Ferrel <[email protected]>: >>> >>>> yes to all for UR v0.5.0 >>>> >>>> UR v0.6.0 is sitting in the `develop` branch waiting for one more minor >>>> fix to be released. It uses the latest release of Mahout 0.13.0 so no need >>>> to build it for the project. Several new features too. I expect it to be >>>> out this week. >>>> >>>> >>>> On May 8, 2017, at 3:07 AM, Dennis Honders <[email protected]> >>>> wrote: >>>> >>>> Hi, >>>> >>>> Are the following docs up-to-date? >>>> >>>> PredictionIO: http://actionml.com/docs/pio_quickstart. >>>> Is version 0.11.0 suitable for UR? >>>> >>>> The UR: http://actionml.com/docs/ur. >>>> Is 0.5.0 the latest version? >>>> Is Mahout still necessary? >>>> >>>> Thanks, >>>> >>>> Dennis >>>> >>>> >>> >>> >> >> > > -- > You received this message because you are subscribed to the Google Groups > "actionml-user" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To post to this group, send email to [email protected]. > To view this discussion on the web visit https://groups.google.com/d/ > msgid/actionml-user/CAC-ATVGvbEM3nzmAPk4%2BD4GM6z1e1t9yJf4irR1kN1y5% > 3DAk4Ag%40mail.gmail.com > <https://groups.google.com/d/msgid/actionml-user/CAC-ATVGvbEM3nzmAPk4%2BD4GM6z1e1t9yJf4irR1kN1y5%3DAk4Ag%40mail.gmail.com?utm_medium=email&utm_source=footer> > . > For more options, visit https://groups.google.com/d/optout. > >
