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From Niklas Ekvall <niklas.ekv...@gmail.com>
Subject Re: Number of features for ALS
Date Sun, 30 Mar 2014 19:41:18 GMT
Hello Sebastian, could you do a deeper explanation or refer to any article
that handle the subject?

Best regards, Niklas


2014-03-30 20:50 GMT+02:00 Sebastian Schelter <ssc@apache.org>:

> Use k-fold cross-validation or hold-out tests for estimating the quality
> of different parameter combinations.
>
> --sebastian
>
>
> On 03/30/2014 11:53 AM, Niklas Ekvall wrote:
>
>> Hi,
>>
>> My name is Niklas Ekvall and I have a implementation of the recommender
>> algorithm "Large-scale Parallel Collaborative Filtering for the Netflix
>> Prize" and now I'm wondering how to choose the number of features and
>> lambda. Could any of guys help me to explain a stepwise strategy to choose
>> or optimize these two parameters?
>>
>> Best regards, Niklas
>>
>>
>> 2014-03-27 19:07 GMT+01:00 j.barrett Strausser <
>> j.barrett.strausser@gmail.com>:
>>
>>  Thanks Ted,
>>>
>>> Yes for the time problem. We tend to use aggregations of session data. So
>>> instead of asking for user recommendations we do things like
>>> user+sessions
>>> recommendations.
>>>
>>> Of course, deciding when sessions start and stop isn't trivial. I ideally
>>> what I would want to is time-weight views using a kernel or convolution.
>>> That's a bit heavy so we typically have a global model, that is is
>>> basically all preferences over times. Then these user+session type
>>> models.
>>> We can then combine these at another level to give recommendations based
>>> on
>>> what you like throughout time versus what you have been doing recently.
>>>
>>>
>>>
>>> -b
>>>
>>>
>>> On Thu, Mar 27, 2014 at 1:59 PM, Ted Dunning <ted.dunning@gmail.com>
>>> wrote:
>>>
>>>  For the poly-syllable challenged,
>>>>
>>>> hetereoscedasticity - degree of variation changes.  This is common with
>>>> counts because you expect the standard deviation of count data to be
>>>> proportional to sqrt(n).
>>>>
>>>> time imhogeneity - changes in behavior over time.  One way to handle
>>>> this
>>>> (roughly) is to first remove variation in personal and item means over
>>>>
>>> time
>>>
>>>> (if using ratings) and then to segment user histories into episodes.  By
>>>> including both short and long episodes you get some repair for changes
>>>> in
>>>> personal preference.  A great example of how this works/breaks is
>>>>
>>> Christmas
>>>
>>>> music.  On December 26th, you want to *stop* recommending this music so
>>>>
>>> it
>>>
>>>> really pays to limit histories at this point.  By having an episodic
>>>> user
>>>> session that starts around November and runs to Christmas, you can get
>>>>
>>> good
>>>
>>>> recommendations for seasonal songs and not pollute the rest of the
>>>> universe.
>>>>
>>>>
>>>>
>>>> On Thu, Mar 27, 2014 at 8:30 AM, j.barrett Strausser <
>>>> j.barrett.strausser@gmail.com> wrote:
>>>>
>>>>  For my team it has usually been hetereoscedasticity and time
>>>>>
>>>> inhomogeneity.
>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Thu, Mar 27, 2014 at 10:18 AM, Tevfik Aytekin
>>>>> <tevfik.aytekin@gmail.com>wrote:
>>>>>
>>>>>  Interesting topic,
>>>>>> Ted, can you give examples of those mathematical assumptions
>>>>>> under-pinning ALS which are violated by the real world?
>>>>>>
>>>>>> On Thu, Mar 27, 2014 at 3:43 PM, Ted Dunning <ted.dunning@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> How can there be any other practical method?  Essentially all
of
>>>>>>>
>>>>>> the
>>>
>>>> mathematical assumptions under-pinning ALS are violated by the real
>>>>>>>
>>>>>> world.
>>>>>>
>>>>>>>   Why would any mathematical consideration of the number of features
>>>>>>>
>>>>>> be
>>>>
>>>>> much
>>>>>>
>>>>>>> more than heuristic?
>>>>>>>
>>>>>>> That said, you can make an information content argument.  You
can
>>>>>>>
>>>>>> also
>>>>
>>>>> make
>>>>>>
>>>>>>> the argument that if you take too many features, it doesn't much
>>>>>>>
>>>>>> hurt
>>>
>>>> so
>>>>>
>>>>>> you should always take as many as you can compute.
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Thu, Mar 27, 2014 at 6:33 AM, Sebastian Schelter <
>>>>>>>
>>>>>> ssc@apache.org>
>>>
>>>> wrote:
>>>>>>
>>>>>>>
>>>>>>>  Hi,
>>>>>>>>
>>>>>>>> does anyone know of a principled approach of choosing the
number
>>>>>>>>
>>>>>>> of
>>>
>>>> features for ALS (other than cross-validation?)
>>>>>>>>
>>>>>>>> --sebastian
>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> --
>>>>>
>>>>>
>>>>> https://github.com/bearrito
>>>>> @deepbearrito
>>>>>
>>>>>
>>>>
>>>
>>>
>>> --
>>>
>>>
>>> https://github.com/bearrito
>>> @deepbearrito
>>>
>>>
>>
>

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