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From Pat Ferrel <...@occamsmachete.com>
Subject Re: "LLR with time"
Date Sun, 12 Nov 2017 21:34:38 GMT
Part of what Ted is talking about can be seen in the carousels on Netflix or Amazon. Some are
not recommendations like “trending” videos, or “new” videos, or “prime” videos
(substitute your own promotions here). Nothing to do with recommender created items but presented
along with recommender-based carousels. They are based on analytics or business rules and
ideally have some randomness built in. The reason for this is 1) it works by exposing users
to items that they would not see in recommendations and 2) it provides data to build the recommender
model from.

A recommender cannot work in an app that has no non-recommended items displayed or there will
be no un-biased data to create recommendations from. This would lead to crippling overfitting.
Most apps have placements like the ones mentioned above and also have search and browse. However
you do it, it must be prominent and aways available. The moral of this paragraph is; don’t
try to make everything a recommendation, it will be self-defeating. In fact make sure not
every video watch comes from a recommendation.

Likewise think of placements (reflecting a particular recommender use) as experimentation
grounds. Try things like finding a recommended category and then recommending items in that
category all based on user behavior. Or try a placement based on a single thing a user watched
like “because you watched xyz you might like these”. Don’t just show the most popular
categories for the user and recommend items in them. This would be a type of overfitting too.

I’m sure we have strayed far from your original question but maybe it’s covered somewhere
in here.


On Nov 12, 2017, at 12:11 PM, Johannes Schulte <johannes.schulte@gmail.com> wrote:

I did "second order" recommendations before but more to fight sparsity and
find more significant associations in situations with less traffic, so
recommending categories instead of products. There needs to be some third
order sorting / boosting like you mentioned with "new music", or maybe
popularity or hotness to avoid quasi-random order. For events with limited
lifetime it's probably some mixture of spatial distance and freshness.

We will definetely keep an eye on the generation process of data for new
items. It depends on the domain but in the time of multi channel promotion
of videos, shows and products, it's also helps that there is traffic driven
from external sources.

Thanks for the detailed  hints - now it's time to see what comes out of
this.

Johannes

On Sun, Nov 12, 2017 at 7:52 AM, Ted Dunning <ted.dunning@gmail.com> wrote:

> Events have the natural good quality that having a cold start means that
> you will naturally favor recent interactions simply because there won't be
> any old interactions to deal with.
> 
> Unfortunately, that also means that you will likely be facing serious cold
> start issues all the time. I have used two strategies to deal with cold
> starts, both fairly successfully.
> 
> *Method 1: Second order recommendation*
> 
> For novel items with no history, you typically do have some kind of
> information about the content. For an event, you may know the performer,
> the organizer, the venue, possibly something about the content of the event
> as well (especially for a tour event). As such, you can build a recommender
> that recommends this secondary information and then do a search with
> recommended secondary information to find events. This actually works
> pretty well, at least for the domains where I have used (music and videos).
> For instance, in music, you can easily recommend a new album based on the
> artist (s) and track list.
> 
> The trick here is to determine when and how to blend in normal
> recommendations. One way is query blending where you combine the second
> order query with a normal recommendation query, but I think that a fair bit
> of experimentation is warranted here.
> 
> *Method 2: What's new and what's trending*
> 
> It is always important to provide alternative avenues of information
> gathering for recommendation. Especially for the user generated video case,
> there was pretty high interest in the "What's new" and "What's hot" pages.
> If you do a decent job of dithering here, you keep reasonably good content
> on the what's new page longer than content that doesn't pull. That
> maintains interest in the page. Similarly, you can have a bit of a lower
> bar for new content to be classified as hot than established content. That
> way you keep the page fresh (because new stuff appears transiently), but
> you also have a fair bit of really good stuff as well. If done well, these
> pages will provide enough interactions with new items so that they don't
> start entirely cold. You may need to have genre specific or location
> specific versions of these pages to avoid interesting content being
> overwhelmed. You might also be able to spot content that has intense
> interest from a sub-population as opposed to diffuse interest from a mass
> population.
> 
> You can also use novelty and trending boosts for content in the normal
> recommendation engine. I have avoided this in the past because I felt it
> was better to have specialized pages for what's new and hot rather than
> because I had data saying it was bad to do. I have put a very weak
> recommendation effect on the what's hot pages so that people tend to see
> trending material that they like. That doesn't help on what's new pages for
> obvious reasons unless you use a touch of second order recommendation.
> 
> 
> 
> 
> 
> On Sat, Nov 11, 2017 at 11:00 PM, Johannes Schulte <
> johannes.schulte@gmail.com> wrote:
> 
>> Well the greece thing was just an example for a thing you don't know
>> upfront - it could be any of the modeled feature on the cross recommender
>> input side (user segment, country, city, previous buys), some
> subpopulation
>> getting active, so the current approach, probably with sampling that
>> favours newer events, will be the best here. Luckily a sampling strategy
> is
>> a big topic anyway since we're trying to go for the near real time way -
>> pat, you talked about it some while ago on this list and i still have to
>> look at the flink talk from trevor grant but I'm really eager to attack
>> this after years of batch :)
>> 
>> Thanks for your thoughts, I am happy I can rule something out given the
>> domain (poisson llr). Luckily the domain I'm working on is event
>> recommendations, so there is a natural deterministic item expiry (as
>> compared to christmas like stuff).
>> 
>> Again,
>> thanks!
>> 
>> 
>> On Sat, Nov 11, 2017 at 7:00 PM, Ted Dunning <ted.dunning@gmail.com>
>> wrote:
>> 
>>> Inline.
>>> 
>>> On Sat, Nov 11, 2017 at 6:31 PM, Pat Ferrel <pat@occamsmachete.com>
>> wrote:
>>> 
>>>> If Mahout were to use http://bit.ly/poisson-llr it would tend to
> favor
>>>> new events in calculating the LLR score for later use in the
> threshold
>>> for
>>>> whether a co or cross-occurrence iss incorporated in the model.
>>> 
>>> 
>>> I don't think that this would actually help for most recommendation
>>> purposes.
>>> 
>>> It might help to determine that some item or other has broken out of
>>> historical rates. Thus, we might have "hotness" as a detected feature
>> that
>>> could be used as a boost at recommendation time. We might also have
> "not
>>> hotness" as a negative boost feature.
>>> 
>>> Since we have a pretty good handle on the "other" counts, I don't think
>>> that the Poisson test would help much with the cooccurrence stuff
> itself.
>>> 
>>> Changing the sampling rule could make a difference to temporality and
>> would
>>> be more like what Johannes is asking about.
>>> 
>>> 
>>>> But it doesn’t relate to popularity as I think Ted is saying.
>>>> 
>>>> Are you looking for 1) personal recommendations biased by hotness in
>>>> Greece or 2) things hot in Greece?
>>>> 
>>>> 1) create a secondary indicator for “watched in some locale” the
>> local-id
>>>> uses a country-code+postal-code maybe but not lat-lon. Something that
>>>> includes a good number of people/events. The the query would be
>> user-id,
>>>> and user-locale. This would yield personal recs preferred in the
> user’s
>>>> locale. Athens-west-side in this case.
>>>> 
>>> 
>>> And this works in the current regime. Simply add location tags to the
>> user
>>> histories and do cooccurrence against content. Locations will pop out
> as
>>> indicators for some content and not for others. Then when somebody
>> appears
>>> in some location, their tags will retrieve localized content.
>>> 
>>> For localization based on strict geography, say for restaurant search,
> we
>>> can just add business rules based on geo-search. A very large bank
>> customer
>>> of ours does that, for instance.
>>> 
>>> 
>>>> 2) split the data into locales and do the hot calc I mention. The
> query
>>>> would have no user-id since it is not personalized but would yield
> “hot
>>> in
>>>> Greece”
>>>> 
>>> 
>>> I think that this is a good approach.
>>> 
>>> 
>>>> 
>>>> Ted’s “Christmas video” tag is what I was calling a business rule and
>> can
>>>> be added to either of the above techniques.
>>>> 
>>> 
>>> But the (not) hotness feature might help with automated this.
>>> 
>>> 
>>> 
>>> 
>>>> 
>>>> On Nov 11, 2017, at 4:01 AM, Ted Dunning <ted.dunning@gmail.com>
>> wrote:
>>>> 
>>>> So ... there are a few different threads here.
>>>> 
>>>> 1) LLR but with time. Quite possible, but not really what Johannes is
>>>> talking about, I think. See http://bit.ly/poisson-llr for a quick
>>>> discussion.
>>>> 
>>>> 2) time varying recommendation. As Johannes notes, this can make use
> of
>>>> windowed counts. The problem is that rarely accessed items should
>>> probably
>>>> have longer windows so that we use longer term trends when we have
> less
>>>> data.
>>>> 
>>>> The good news here is that this some part of this is nearly already
> in
>>> the
>>>> code. The trick is that the down-sampling used in the system can be
>>> adapted
>>>> to favor recent events over older ones. That means that if the
> meaning
>> of
>>>> something changes over time, the system will catch on. Likewise, if
>>>> something appears out of nowhere, it will quickly train up. This
>> handles
>>>> the popular in Greece right now problem.
>>>> 
>>>> But this isn't the whole story of changing recommendations. Another
>>> problem
>>>> that we commonly face is what I call the christmas music issue. The
>> idea
>>> is
>>>> that there are lots of recommendations for music that are highly
>>> seasonal.
>>>> Thus, Bing Crosby fans want to hear White Christmas
>>>> <https://www.youtube.com/watch?v=P8Ozdqzjigg> until the day after
>>>> christmas
>>>> at which point this becomes a really bad recommendation. To some
>> degree,
>>>> this can be partially dealt with by using temporal tags as
> indicators,
>>> but
>>>> that doesn't really allow a recommendation to be completely shut
> down.
>>>> 
>>>> The only way that I have seen to deal with this in the past is with a
>>>> manually designed kill switch. As much as possible, we would tag the
>>>> obviously seasonal content and then add a filter to kill or downgrade
>>> that
>>>> content the moment it went out of fashion.
>>>> 
>>>> 
>>>> 
>>>> On Sat, Nov 11, 2017 at 9:43 AM, Johannes Schulte <
>>>> johannes.schulte@gmail.com> wrote:
>>>> 
>>>>> Pat, thanks for your help. especially the insights on how you
> handle
>>> the
>>>>> system in production and the tips for multiple acyclic buckets.
>>>>> Doing the combination signalls when querying sounds okay but as you
>>> say,
>>>>> it's always hard to find the right boosts without setting up some
> ltr
>>>>> system. If there would be a way to use the hotness when calculating
>> the
>>>>> indicators for subpopulations it would be great., especially for a
>>> cross
>>>>> recommender.
>>>>> 
>>>>> e.g. people in greece _now_ are viewing this show/product  whatever
>>>>> 
>>>>> And here the popularity of the recommended item in this
> subpopulation
>>>> could
>>>>> be overrseen when just looking at the overall derivatives of
>> activity.
>>>>> 
>>>>> Maybe one could do multiple G-Tests using sliding windows
>>>>> * itemA&itemB  vs population (classic)
>>>>> * itemA&itemB(t) vs itemA&itemB(t-1)
>>>>> ..
>>>>> 
>>>>> and derive multiple indicators per item to be indexed.
>>>>> 
>>>>> But this all relies on discretizing time into buckets and not
> looking
>>> at
>>>>> the distribution of time between events like in presentation above
> -
>>>> maybe
>>>>> there is  something way smarter
>>>>> 
>>>>> Johannes
>>>>> 
>>>>> On Sat, Nov 11, 2017 at 2:50 AM, Pat Ferrel <pat@occamsmachete.com
>> 
>>>> wrote:
>>>>> 
>>>>>> BTW you should take time buckets that are relatively free of daily
>>>> cycles
>>>>>> like 3 day, week, or month buckets for “hot”. This is to remove
>>> cyclical
>>>>>> affects from the frequencies as much as possible since you need 3
>>>> buckets
>>>>>> to see the change in change, 2 for the change, and 1 for the event
>>>>> volume.
>>>>>> 
>>>>>> 
>>>>>> On Nov 10, 2017, at 4:12 PM, Pat Ferrel <pat@occamsmachete.com>
>>> wrote:
>>>>>> 
>>>>>> So your idea is to find anomalies in event frequencies to detect
>> “hot”
>>>>>> items?
>>>>>> 
>>>>>> Interesting, maybe Ted will chime in.
>>>>>> 
>>>>>> What I do is take the frequency, first, and second, derivatives as
>>>>>> measures of popularity, increasing popularity, and increasingly
>>>>> increasing
>>>>>> popularity. Put another way popular, trending, and hot. This is
>> simple
>>>> to
>>>>>> do by taking 1, 2, or 3 time buckets and looking at the number of
>>>> events,
>>>>>> derivative (difference), and second derivative. Ranking all items
> by
>>>>> these
>>>>>> value gives various measures of popularity or its increase.
>>>>>> 
>>>>>> If your use is in a recommender you can add a ranking field to all
>>> items
>>>>>> and query for “hot” by using the ranking you calculated.
>>>>>> 
>>>>>> If you want to bias recommendations by hotness, query with user
>>> history
>>>>>> and boost by your hot field. I suspect the hot field will tend to
>>>>> overwhelm
>>>>>> your user history in this case as it would if you used anomalies
> so
>>>> you’d
>>>>>> also have to normalize the hotness to some range closer to the one
>>>>> created
>>>>>> by the user history matching score. I haven’t found a vey good
way
>> to
>>>> mix
>>>>>> these in a model so use hot as a method of backfill if you cannot
>>> return
>>>>>> enough recommendations or in places where you may want to show
> just
>>> hot
>>>>>> items. There are several benefits to this method of using hot to
>> rank
>>>> all
>>>>>> items including the fact that you can apply business rules to them
>>> just
>>>>> as
>>>>>> normal recommendations—so you can ask for hot in “electronics”
if
>> you
>>>>> know
>>>>>> categories, or hot "in-stock" items, or ...
>>>>>> 
>>>>>> Still anomaly detection does sound like an interesting approach.
>>>>>> 
>>>>>> 
>>>>>> On Nov 10, 2017, at 3:13 PM, Johannes Schulte <
>>>>> johannes.schulte@gmail.com>
>>>>>> wrote:
>>>>>> 
>>>>>> Hi "all",
>>>>>> 
>>>>>> I am wondering what would be the best way to incorporate event
> time
>>>>>> information into the calculation of the G-Test.
>>>>>> 
>>>>>> There is a claim here
>>>>>> https://de.slideshare.net/tdunning/finding-changes-in-real-data
>>>>>> 
>>>>>> saying "Time aware variant of G-Test is possible"
>>>>>> 
>>>>>> I remember i experimented with exponentially decayed counts some
>> years
>>>>> ago
>>>>>> and this involved changing the counts to doubles, but I suspect
>> there
>>> is
>>>>>> some smarter way. What I don't get is the relation to a data
>> structure
>>>>> like
>>>>>> T-Digest when working with a lot of counts / cells for every
>>> combination
>>>>> of
>>>>>> items. Keeping a t-digest for every combination seems unfeasible.
>>>>>> 
>>>>>> How would one incorporate event time into recommendations to
> detect
>>>>>> "hotness" of certain relations? Glad if someone has an idea...
>>>>>> 
>>>>>> Cheers,
>>>>>> 
>>>>>> Johannes
>>>>>> 
>>>>>> 
>>>>>> 
>>>>> 
>>>> 
>>>> 
>>> 
>> 
> 


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