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From Marius Rabenarivo <mariusrabenar...@gmail.com>
Subject Re: Docs Universal Recommender
Date Wed, 10 May 2017 20:05:48 GMT
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 <pat@occamsmachete.com>:

> 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 <
> mariusrabenarivo@gmail.com> wrote:
>
> Hello,
>
> So to what does the matrix T and vector h_t in this slide match to? :
> https://docs.google.com/presentation/d/1MzIGFsATNeAYnLfoR6797ofcLeFRK
> SX7KB8GAYNtNPY/edit#slide=id.gf4d43b9e8_1_24
>
> 2017-05-10 21:10 GMT+04:00 Pat Ferrel <pat@occamsmachete.com>:
>
>> 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 <dennishonders@gmail.com>
>> 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 <pat@occamsmachete.com>:
>>
>>> 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 <dennishonders@gmail.com>
>>> 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
>>>
>>>
>>
>>
>
>

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