I’ve been intrigued with Netflix recommendation systems lately as we’ve been watching some of these videos online and the system is ‘recommending’ choices – not overly well I might add: lots of room for improvement. Earlier I had read about the Netflix prize contest through a Wired article http://www.wired.com/techbiz/media/magazine/16-03/mf_netflix – more focused on psychologiist applying this by syntehsizing behavioralist economics ‘decision making’ heurisitcs (Tversky, Kahneman). This seems like a worthy line with these ‘heuristic’ decision methods to be quantified through algorithms. I do also think there’s a lot of room to move with these recommender systems by combining Luis von Ahn’s game idea’s regarding ‘games with a purpose’ with more old guard computer science recommender system challenges and say slying reconfiguring these by appropriating uthe Netflix platform to build these databases such as von Ahn is currently achieve with the Google Image Labeler.

A friend of mine sent me his recent Communications of the ACM Volume 51, Number 8 (2008), Pages 57-67, games with a purpose structural article which isn’t overly great (I would have liked more structural step by step alogrithmic recipe rather than justificatory methodology – I suppose this is more of a hard sell with the doyenne of the ACM crrowd but perhaps this will arrive later with the book! As you were on one of his design teams, why not implement some of these methodologies as an online ‘film recommendation’ game system that also takes advantage of other users: apparently the Netflix database is open through the prize. (On another note, there seems to be a whole level of paratextual data – other users blogging on movies they ‘liked’, mentioning others that could also be incorporated into this level of the database and this data doesn’t seem to be ‘in’ the recommender system).

Here’s what I presently don’t like about the online Netflix recommendation system (suggestions for future fixes improvements)

1) It doesn’t take into account that there are different users in my house making use of the subscription (a) baby daughter b)wife c)myself. – I like science fiction and documentary, my wife likes romantic comedy and my baby likes Scooby Doo and Word World – this diversity really throws wrench into the recommendations and the system is not intelligent enough to see the ‘large probabilistic’ differences between ‘genres’

2) the star rating system seems too facile: challenge of making this more defined while not alienating the user

3)the film and generic taxonomies seems too rigid and from the top down – perhaps this taxonomy can be organically generated and built somehow as ‘part’ of the recommendation system, more of a ‘folksonomy’ rather than taxonomy: this could be part of the game

4) There is no hiearchical definitions in the current ratings system between movies watched through the mail and movies watched instantly – we are more likely to watch the ones instantly as we watch everything through the computer and can’t wait for the mail: we limit our choices in the mail to those we really want; this should be taken into account in the system weightings.

5) There is a book I believe called “Discrimination” about Discriminating taste by Pierre Bourdieu – French Poststructuralist – some of this info I believe which is ‘class’ related could be useful worked into algorithms and quantified – the algorithm should quantify some of his ideas similar to the behavioral/quantification that the wired psychologist is doing.

This is also related to and combines a couple of your other recent ‘jog search’ posts but I also believe perhaps Netflix would be a good place to be working on these kind of problems as this is a great challenge and I do believe their ‘watch movies’ instantly will soon takeover any ‘physical’ based media ‘mail’ transfer. You also mentioned concept maps though in a recent entry and I believe this place here http://www.ihmc.us/openings.php has a history of working on some areas involving computational linguistics – its located in the city where I’m currently at and they always have an interesting lecture series. We also brought in their “concept maps” tools people http://cmap.ihmc.us/ for a couple lectures here at the univeristy for our digital learning and technology series http://www.uwf.edu/ruzwyshyn/2007Workshops/DigitalForumSeries.html -Concept maps seem to have been around and while I would have liked to use these for our ‘subject guides’ here – this hasn’t overly taken root with the appropriate sub departments – there does seem to room also for synergies between say recommender systems/games/concept maps and taking this to the next level.

Finally, though we don’t have the money to hire someone like you currently, I’m intrigued by what a computational linguist would do with some of our university library catalog tracking data http://library.uwf.edu/uwf_2007_08_report.htm which currently has the ability to track every keystroke into our system in interesting manners. We started on a statistical project regarding this here http://library.uwf.edu/endecastatistics.htm but this really does need a higher level of say application of mathematics with computational linguistics to think more synthetically about what the system is producing. Perhaps a meta- recommender system could be built connecting academics in disparate parts of the world on the same topics ‘from different angles’ at the same moment better together by the knowledge they are searching for and research they are doing (kind of like Netflix for academic research rather than movies: at any rate, ever you ever want to use any of this data feel free – but please combine it with von Ahn’s game idea. In his article, he does stress ‘the enjoyability’ factor and I do think he is onto something here to say the least