About the 'user who watches too many movies' problem: is it worth recasting the item list by genre? That is, he watched one out of five available movies, but they were 90% Sci-fi and Westerns. (Definitely male :) Is it worth recasting the item counts as generic votes for Sci-Fi and Westerns?
On Sun, Aug 26, 2012 at 5:17 PM, Jonathan Hodges <[email protected]> wrote: > Thanks for your thorough response. It is really helpful as we are new to > Mahout and recommendations in general. The approach you mention about > training on data up to a certain point a time and having the recommender > score the next actual observations is very interesting. This would seem to > work well with our Boolean dataset. We will give this a try. > > > Thanks again for the help. > > > -Jonathan > > > On Sun, Aug 26, 2012 at 3:55 PM, Sean Owen <[email protected]> wrote: > >> Most watched by that particular user. >> >> The issue is that the recommender is trying to answer, "of all items >> the user has not interacted with, which is the user most likely to >> interact with"? So the 'right answers' to the quiz it gets ought to be >> answers to this question. That is why the test data ought to be what >> appears to be the most interacted / preferred items. >> >> For example If you watched 10 Star Trek episodes, then 1 episode of >> the Simpsons, and then held out the Simpson episode -- the recommender >> is almost surely not going to predict it, not above more Star Trek. >> That seems like correct behavior, but would be scored badly by a >> simple precision test. >> >> There are two downsides to this approach. Firstly removing well liked >> items from the training set may meaningfully skew a user's >> recommendations. It's not such a big issue if the test set is small -- >> and it should be. >> >> The second is that by taking out data this way you end up with a >> training set which never really existed at one point in time. That >> also could be a source of bias. >> >> Using recent data points tends to avoid both of these problem -- but >> then has the problem above. >> >> >> There's another approach I've been playing with, which works when the >> recommender produces some score for each rec, not just a ranked list. >> You can train on data up to a certain point in time, then have the >> recommender score the observations that really happened after that >> point. Ideally it should produce a high score for things that really >> were observed next. >> >> This isn't implemented in Mahout but you do get a score with recs even >> without ratings. >> -- Lance Norskog [email protected]
