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.schu...@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 <p...@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 <p...@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.schu...@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 > > > > > > >
