Archive for 11 July 2009

Learning Scala

Posted: 11 July 2009 in Uncategorized
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Programming in Scala

Programming in Scala

Two weeks ago, I picked up my copy of Programming in Scala, which had been languishing on my shelf for months.  I pre-purchased it since I went to high school with one of the authors (Lex Spoon).  His mother, incidentally, was also my favorite math teacher.  When I started my new job back in September 2008, I was a total noob at Ruby, so learning that consumed my attention and other languages took a back burner.  Also, I’m always a little reluctant when it comes to learning new languages.  Not because I don’t like to learn them or because it’s difficult — but because it’s a serious investment of time that may be totally wasted.  Sure, Standard ML is an interesting language, but try finding a job doing it.  When I heard that Twitter was using Scala, I figured the time has come to pick up this book.  It also helped that a friend recently started an Atlanta Scala Meetup group.

Aside from being an update on my life, the point of this post is to say that this book is great.  Seldom have I encountered a programming book that achieves this level of depth while still being fun to read.  There are great examples with humor mixed in, the writing is clear and concise, and it’s thorough. What more could you want?

Has anyone else picked up Scala?  (I know there’s a few of you out there lurking!)  Are there any other good books you would recommend?

In the interest of full disclosure, though I know one of the authors, I haven’t actually talked to him in quite a long time (since high school, I think).  I also don’t make any extra money aside from the Amazon affiliate program commission if you happen to buy anything on their site after clicking the book link.

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R.I.P. Movie Rating Prediction

R.I.P. Movie Rating Prediction

There is no longer any reason to bother researching new ways of predicting the ratings users will give to movies.  It’s time to move on to more interesting things.  But seriously, given the fact that the last few miles of the Netflix competition were hard-fought by combining hundreds of different algorithms, is there much value in trying to improve recommender systems in this way, anymore?

I expect that the Netflix Prize data set, if left open to the public, will still be useful for a number of machine learning tasks where the goal is not necessarily improving recommender systems.  So predicting movie ratings may never be really dead.  But it is my hope that that as a goal for research will diminish and the focus will start moving towards other aspects of recommender systems still greatly lacking.  Like building systems that facilitate discovery of new items.

Factoring in the temporal dimension was a big deal in the latter part of the competition.  Sometimes you’re just in the mood for something gloomy.  Or something funny.  Or something ridiculous.  The same movie may totally turn you off a week later.  No machine (biological or mechanical) can predict these swings of emotions in the near future, so why bother?  Flip that around and let’s find ways of improving the search for items matching our mood at the time.

A system that interactively elicits your mood and guides you to matching items would be incredibly useful, don’t you think?